[refactor] refactor one fold fit, enable to get sorted estimators with other...

[refactor] refactor one fold fit, enable to get sorted estimators with other selection criterion without more computation

extreme_trend/one_fold_fit/one_fold_fit.py

@@ -181,7 +181,24 @@ class OneFoldFit(object):
     # Minimizing the AIC and some properties
 
     @cached_property
-    def sorted_estimators(self):
+    def sorted_estimators_with_aic(self):
+        return self._sorted_estimators_with_method_name(method_name='aic')
+
+    def method_name_to_best_estimator(self, method_names):
+        return {self._sorted_estimators_with_method_name(method_name) for method_name in method_names}
+
+    def _sorted_estimators_with_method_name(self, method_name):
+        estimators = self.estimators_quality_checked
+        try:
+            sorted_estimators = sorted([estimator for estimator in estimators],
+                                       key=lambda e: e.__getattribute__(method_name))
+        except AssertionError as e:
+            print('Error for:\n', self.massif_name, self.altitude_group)
+            raise e
+        return sorted_estimators
+
+    @cached_property
+    def estimators_quality_checked(self):
         estimators = list(self.model_class_to_estimator.values())
         if self.remove_physically_implausible_models:
             # Remove wrong shape
@@ -211,20 +228,12 @@ class OneFoldFit(object):
 
             if len(estimators) == 0:
                 print(self.massif_name, " has only implausible models")
-
-        try:
-            sorted_estimators = sorted([estimator for estimator in estimators], key=lambda e: e.aic)
-        except AssertionError as e:
-            print('Error for')
-            print(self.massif_name, self.altitude_group)
-            raise
-
         #  Apply the goodness of fit
         if self.only_models_that_pass_goodness_of_fit_test:
-            return [e for e in sorted_estimators if self.goodness_of_fit_test(e)]
+            estimators = [e for e in estimators if self.goodness_of_fit_test(e)]
         if not (self.remove_physically_implausible_models or self.only_models_that_pass_goodness_of_fit_test):
-            assert len(sorted_estimators) == len(self.models_classes)
-        return sorted_estimators
+            assert len(estimators) == len(self.models_classes)
+        return estimators
 
     def get_coordinate(self, altitude, year):
         if isinstance(self.altitude_group, DefaultAltitudeGroup):
@@ -244,16 +253,16 @@ class OneFoldFit(object):
 
     @property
     def has_at_least_one_valid_model(self):
-        return len(self.sorted_estimators) > 0
+        return len(self.sorted_estimators_with_aic) > 0
 
     @property
     def model_class_to_estimator_with_finite_aic(self):
-        return {type(estimator.margin_model): estimator for estimator in self.sorted_estimators}
+        return {type(estimator.margin_model): estimator for estimator in self.sorted_estimators_with_aic}
 
     @property
     def best_estimator(self):
         if self.has_at_least_one_valid_model:
-            best_estimator = self.sorted_estimators[0]
+            best_estimator = self.sorted_estimators_with_aic[0]
             return best_estimator
         else:
             raise ValueError('This object should not have been called because '
@@ -289,7 +298,7 @@ class OneFoldFit(object):
 
     @property
     def model_names(self):
-        return [e.margin_model.name_str for e in self.sorted_estimators]
+        return [e.margin_model.name_str for e in self.sorted_estimators_with_aic]
 
     @property
     def best_name(self):