fix score_cache, fixes #15

evolutionary_search/__init__.py

@@ -74,26 +74,23 @@ def _individual_to_params(individual, name_values):
     return dict((name, values[gene]) for gene, (name, values) in zip(individual, name_values))
 
 
-def _evalFunction(individual, searchobj, name_values, X, y, scorer, cv, iid, fit_params,
+score_cache = {}
+
+def _evalFunction(individual, name_values, X, y, scorer, cv, iid, fit_params,
                   verbose=0, error_score='raise'):
     parameters = _individual_to_params(individual, name_values)
     score = 0
     n_test = 0
     for train, test in cv:
-        paramkey = str(parameters)
-        if paramkey in searchobj.score_cache:
-            searchobj.num_cache_hits += 1
-            _score = searchobj.score_cache[paramkey]
+        paramkey = str(individual)
+        if paramkey in score_cache:
+            _score = score_cache[paramkey]
         else:
             _score, _, _ = _fit_and_score(estimator=individual.est, X=X, y=y, scorer=scorer,
                                      train=train, test=test, verbose=verbose,
                                      parameters=parameters, fit_params=fit_params,
                                      error_score=error_score)
-            searchobj.num_evaluations += 1
-            searchobj.score_cache[paramkey] = _score
-        if searchobj.verbose and (searchobj.num_evaluations + searchobj.num_cache_hits) % searchobj.population_size == 0:
-            print("Scoring evaluations: %d, Cache hits: %d, Total: %d" % (
-                searchobj.num_evaluations, searchobj.num_cache_hits, searchobj.num_evaluations + searchobj.num_cache_hits))
+            score_cache[paramkey] = _score
         if iid:
             score += _score * len(test)
             n_test += len(test)
@@ -272,9 +269,6 @@ def __init__(self, estimator, params, scoring=None, cv=4,
         self.gene_crossover_prob = gene_crossover_prob
         self.tournament_size = tournament_size
         self.gene_type = gene_type
-        self.score_cache = {}
-        self.num_cache_hits = 0
-        self.num_evaluations = 0
 
     def fit(self, X, y=None):
         self.best_estimator_ = None
@@ -316,7 +310,7 @@ def _fit(self, X, y, parameter_dict):
         toolbox.register("individual", _initIndividual, creator.Individual, maxints=maxints)
         toolbox.register("population", tools.initRepeat, list, toolbox.individual)
 
-        toolbox.register("evaluate", _evalFunction, searchobj=self,
+        toolbox.register("evaluate", _evalFunction,
                          name_values=name_values, X=X, y=y,
                          scorer=self.scorer_, cv=cv, iid=self.iid, verbose=self.verbose,
                          error_score=self.error_score, fit_params=self.fit_params)
@@ -349,8 +343,6 @@ def _fit(self, X, y, parameter_dict):
         if self.verbose:
             print("Best individual is: %s\nwith fitness: %s" % (
                 current_best_params_, current_best_score_))
-            print("Scoring evaluations: %d, Cache hits: %d, Total: %d" % (
-                self.num_evaluations, self.num_cache_hits, self.num_evaluations + self.num_cache_hits))
 
         if current_best_score_ > self.best_score_:
             self.best_score_ = current_best_score_

setup.py

@@ -4,12 +4,12 @@
 
 setup(
     name='sklearn-deap',
-    version='0.1.6',
+    version='0.1.7',
     author='Rodrigo',
     author_email='',
     description='Use evolutionary algorithms instead of gridsearch in scikit-learn.',
     url='https://github.com/rsteca/sklearn-deap',
-    download_url='https://github.com/rsteca/sklearn-deap/tarball/0.1.6',
+    download_url='https://github.com/rsteca/sklearn-deap/tarball/0.1.7',
     classifiers=[
         'Development Status :: 4 - Beta',
         'Programming Language :: Python :: 2',