[contrasting] add dctionary to save goodness of fit results

extreme_fit/model/margin_model/polynomial_margin_model/utils.py

@@ -51,20 +51,20 @@ ALTITUDINAL_GEV_MODELS = [
                              NonStationaryAltitudinalLocationQuadraticScaleLinearCrossTermForLocation,
 
                              # Quadratic in the scale
-                             NonStationaryAltitudinalScaleQuadratic,
-                             NonStationaryAltitudinalLocationLinearScaleQuadratic,
-                             NonStationaryAltitudinalLocationQuadraticScaleQuadratic,
-                             NonStationaryAltitudinalScaleQuadraticCrossTermForLocation,
-                             NonStationaryAltitudinalLocationLinearScaleQuadraticCrossTermForLocation,
-                             NonStationaryAltitudinalLocationQuadraticScaleQuadraticCrossTermForLocation,
-
-                             # Cross term for the scale
-                             NonStationaryAltitudinalScaleLinearCrossTermForScale,
-                             NonStationaryAltitudinalLocationLinearScaleLinearCrossTermForScale,
-                             NonStationaryAltitudinalLocationQuadraticScaleLinearCrossTermForScale,
-                             NonStationaryAltitudinalScaleQuadraticCrossTermForScale,
-                             NonStationaryAltitudinalLocationLinearScaleQuadraticCrossTermForScale,
-                             NonStationaryAltitudinalLocationQuadraticScaleQuadraticCrossTermForScale,
+                             # NonStationaryAltitudinalScaleQuadratic,
+                             # NonStationaryAltitudinalLocationLinearScaleQuadratic,
+                             # NonStationaryAltitudinalLocationQuadraticScaleQuadratic,
+                             # NonStationaryAltitudinalScaleQuadraticCrossTermForLocation,
+                             # NonStationaryAltitudinalLocationLinearScaleQuadraticCrossTermForLocation,
+                             # NonStationaryAltitudinalLocationQuadraticScaleQuadraticCrossTermForLocation,
+                             #
+                             # # Cross term for the scale
+                             # NonStationaryAltitudinalScaleLinearCrossTermForScale,
+                             # NonStationaryAltitudinalLocationLinearScaleLinearCrossTermForScale,
+                             # NonStationaryAltitudinalLocationQuadraticScaleLinearCrossTermForScale,
+                             # NonStationaryAltitudinalScaleQuadraticCrossTermForScale,
+                             # NonStationaryAltitudinalLocationLinearScaleQuadraticCrossTermForScale,
+                             # NonStationaryAltitudinalLocationQuadraticScaleQuadraticCrossTermForScale,
 
                          ][:]
 

projects/altitude_spatial_model/altitudes_fit/main_altitudes_studies.py

@@ -34,14 +34,15 @@ def plot_moments(studies, massif_names=None):
 
 def plot_altitudinal_fit(studies, massif_names=None):
     model_classes = ALTITUDINAL_GEV_MODELS
+    # model_classes = ALTITUDINAL_GEV_MODELS_QUADRATIC
     visualizer = AltitudesStudiesVisualizerForNonStationaryModels(studies=studies,
                                                                   model_classes=model_classes,
                                                                   massif_names=massif_names,
                                                                   show=False)
     # Plot the results for the model that minimizes the individual aic
-    # plot_individual_aic(visualizer)
+    plot_individual_aic(visualizer)
     # Plot the results for the model that minimizes the total aic
-    plot_total_aic(model_classes, visualizer)
+    # plot_total_aic(model_classes, visualizer)
 
 
 def main():

projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/one_fold_fit.py

@@ -34,6 +34,8 @@ class OneFoldFit(object):
 
         # Best estimator definition
         self.best_estimator_class_for_total_aic = None
+        # Cached object
+        self._folder_to_goodness = {}
 
     @classproperty
     def folder_for_plots(cls):
@@ -160,13 +162,17 @@ class OneFoldFit(object):
 
     @property
     def goodness_of_fit_anderson_test(self):
-        quantiles = self.compute_empirical_quantiles(estimator=self.best_estimator)
-        goodness_of_fit_anderson_test = goodness_of_fit_anderson(quantiles, self.SIGNIFICANCE_LEVEL)
-        if not goodness_of_fit_anderson_test:
-            print('{} with {} does not pass the anderson test for model {}'.format(self.massif_name,
-                                                                                   self.folder_for_plots,
-                                                                                   type(self.best_margin_model)))
-        return goodness_of_fit_anderson_test
+        if self.folder_for_plots in self._folder_to_goodness:
+            return self._folder_to_goodness[self.folder_for_plots]
+        else:
+            quantiles = self.compute_empirical_quantiles(estimator=self.best_estimator)
+            goodness_of_fit_anderson_test = goodness_of_fit_anderson(quantiles, self.SIGNIFICANCE_LEVEL)
+            if not goodness_of_fit_anderson_test:
+                print('{} with {} does not pass the anderson test for model {}'.format(self.massif_name,
+                                                                                       self.folder_for_plots,
+                                                                                       type(self.best_margin_model)))
+            self._folder_to_goodness[self.folder_for_plots] = goodness_of_fit_anderson_test
+            return goodness_of_fit_anderson_test
 
     def compute_empirical_quantiles(self, estimator):
         empirical_quantiles = []

projects/altitude_spatial_model/altitudes_fit/one_fold_analysis/plot_total_aic.py

@@ -11,10 +11,10 @@ from projects.exceeding_snow_loads.utils import dpi_paper1_figure
 
 
 def plots(visualizer):
-    visualizer.plot_moments()
-    visualizer.plot_best_coef_maps()
     visualizer.plot_shape_map()
     visualizer.plot_year_for_the_peak()
+    visualizer.plot_moments()
+    visualizer.plot_best_coef_maps()
 
 
 def plot_individual_aic(visualizer):