debug

pyrimidine/base.py

@@ -111,8 +111,15 @@ class BaseChromosome(FitnessMixin, metaclass=MetaArray):
     "n_chromosomes": "n_elements"
     }
 
+    @property
+    def class_name(self):
+        if hasattr(self.__class__, '_name'):
+            return self.__class__._name
+        else:
+            return self.__class__.__name__
+
     def __repr__(self):
-        return f'{self.__class__.__name__}: {":".join(map(repr, self))}'
+        return f'{self.class_name}: {":".join(map(repr, self))}'
 
     def __str__(self):
         return "|".join(map(str, self))
@@ -462,7 +469,7 @@ def dual(self):
         return self.__class__([c.dual() for c in self])
 
 
-class BaseMultiPopulation(PopulationMixin, metaclass=MetaHighContainer):
+class BaseMultiPopulation(MultiPopulationMixin, metaclass=MetaHighContainer):
     """Base class of BaseMultiPopulation
     
     Attributes:
@@ -477,7 +484,7 @@ class BaseMultiPopulation(PopulationMixin, metaclass=MetaHighContainer):
 
     alias = {'populations': 'elements',
     'n_populations': 'n_elements',
-    'best_population': 'best_element',
+    'get_best_populationspopulation': 'best_element',
     'worst_population': 'worst_element',
     'get_best_population': 'get_best_element',
     'get_best_populations': 'get_best_elements'
@@ -511,9 +518,6 @@ def mutate(self):
     def mate(self):
         raise NotImplementedError
 
-    # def max_fitness(self):
-    #     return max(map(attrgetter('max_fitness'), self))
-
     def get_best_individual(self, copy=True):
         bests = map(methodcaller('get_best_individual'), self)
         k = max(b.fitness for b in bests)

pyrimidine/meta.py

@@ -330,8 +330,6 @@ def __call__(self, *args, **kwargs):
         for k, v in kwargs.items():
             setattr(o, k, v)
 
-        o.element_type = o.element_class
-
         return o
 
     def __getitem__(self, class_):
@@ -359,6 +357,7 @@ def __ifloordiv__(self, n):
     def __floordiv__(self, n):
         class cls(self):
             default_size = n
+        cls._name = self.__name__
         return cls
 
     def random(self, n_elements=None, *args, **kwargs):
@@ -554,5 +553,6 @@ def __ifloordiv__(self, n):
     def __floordiv__(self, n):
         class cls(self):
             default_size = n
+        cls._name = self.__name__
         return cls
 

pyrimidine/mixin.py

@@ -19,6 +19,7 @@
 """
 
 
+from itertools import chain
 from operator import methodcaller, attrgetter
 
 import numpy as np
@@ -62,10 +63,10 @@ def local_search(self, *args, **kwargs):
         """
         raise NotImplementedError('If you apply a local search algorithm, you must define the `local_search` method.')
 
-    def ezolve(self, max_iter=None, init=True):
+    def ezolve(self, max_iter=None, initialize=True):
         # Extreamly eazy evolution method for lazybones
         max_iter = max_iter or self.max_iter
-        if init:
+        if initialize:
             self.init()
         for k in range(1, max_iter+1):
             self.transition(k)
@@ -108,12 +109,11 @@ def evolve(self, initialize:bool=True, max_iter:int=100, period:int=1, verbose:b
 
         if verbose:
             def _row(t, attrs, res, sep=" & "):
-                return f'{sep.join(map(str, concat((("[%d]"%t,), (getattr(self, attr) for attr in attrs), res.values()))))}'
-            from toolz.itertoolz import concat
+                return sep.join(map(str, chain(("[%d]"%t,), (getattr(self, attr) for attr in attrs), res.values())))
             if not history_flag:
                 res = stat(self)
             print(f"""            ** History **
-{" & ".join(concat((("iteration",), attrs, res.keys())))}
+{" & ".join(chain(("iteration",), attrs, res.keys()))}
 -------------------------------------------------------------""")
             print(_row(0, attrs, res))
 
@@ -122,11 +122,13 @@ def _row(t, attrs, res, sep=" & "):
 
             if history_flag and (period == 1 or t % period ==0):
                 res = stat(self)
-                history = pd.concat([history,
+                history = pd.chain([history,
                     pd.Series(res.values(), index=res.keys()).to_frame().T],
                     ignore_index=True)
 
             if verbose and (period == 1 or t % period ==0):
+                if not history_flag:
+                    res = stat(self)
                 print(_row(t, attrs, res))
 
             if control:
@@ -375,7 +377,7 @@ def evolve(self, stat=None, *args, **kwargs):
         """
 
         if stat is None:
-            stat = {'Best Fitness': 'max_fitness', 'Mean Fitness': 'mean_fitness',
+            stat = {'Max Fitness': 'max_fitness', 'Mean Fitness': 'mean_fitness',
             'STD Fitness': 'std_fitness'}
         return super().evolve(stat=stat, *args, **kwargs)
 
@@ -547,4 +549,12 @@ def drop(self, n=1):
         elif not isinstance(n, int):
             n = int(n)
         ks = self.argsort()
-        self.elements = [self[k] for k in ks[n:]]
+        self.elements = [self[k] for k in ks[n:]]
+
+
+class MultiPopulationMixin(PopulationMixin):
+
+    @property
+    def solution(self):
+        return self.best_element.solution()
+