[contrasting project] fix the definition of deviance. and adapt the code accordingly

extreme_fit/model/result_from_model_fit/abstract_result_from_model_fit.py

@@ -43,7 +43,7 @@ class AbstractResultFromModelFit(object):
 
     @property
     def deviance(self):
-        return - 2 * self.nllh
+        return 2 * self.nllh
 
     @property
     def bic(self):

extreme_trend/abstract_gev_trend_test.py

@@ -76,12 +76,14 @@ class AbstractGevTrendTest(object):
 
     @property
     def aic(self):
-        # deviance = - 2 * nllh
-        return 2 * self.total_number_of_parameters_for_unconstrained_model - self.unconstrained_model_deviance
+        aic = 2 * self.total_number_of_parameters_for_unconstrained_model + self.unconstrained_model_deviance
+        assert np.equal(self.unconstrained_estimator.result_from_model_fit.aic, aic)
+        return aic
 
     @property
     def likelihood_ratio(self):
-        return self.unconstrained_model_deviance - self.constrained_model_deviance
+        assert self.unconstrained_model_deviance < self.constrained_model_deviance
+        return self.constrained_model_deviance - self.unconstrained_model_deviance
 
     @property
     def constrained_model_deviance(self):
@@ -98,14 +100,6 @@ class AbstractGevTrendTest(object):
         else:
             return unconstrained_estimator.result_from_model_fit.deviance
 
-    @property
-    def unconstained_nllh(self):
-        unconstrained_estimator = self.unconstrained_estimator
-        if self.crashed:
-            return np.nan
-        else:
-            return unconstrained_estimator.result_from_model_fit.nllh
-
     # Evolution of the GEV parameters and corresponding quantiles
 
     @property

projects/exceeding_snow_loads/section_results/main_result_trends_and_return_levels.py

 from multiprocessing.pool import Pool
 
 import matplotlib as mpl
+
+from projects.exceeding_snow_loads.section_results.plot_trend_curves import plot_trend_map
+
 mpl.rcParams['text.usetex'] = True
 mpl.rcParams['text.latex.preamble'] = [r'\usepackage{amsmath}']
 
@@ -61,9 +64,9 @@ def intermediate_result(altitudes, massif_names=None,
             _ = compute_minimized_aic(visualizer)
 
     # Plots
-    # plot_trend_map(altitude_to_visualizer)
+    plot_trend_map(altitude_to_visualizer)
     # plot_trend_curves(altitude_to_visualizer={a: v for a, v in altitude_to_visualizer.items() if a >= 900})
-    plot_uncertainty_massifs(altitude_to_visualizer)
+    # plot_uncertainty_massifs(altitude_to_visualizer)
     # plot_uncertainty_histogram(altitude_to_visualizer)
     # plot_selection_curves(altitude_to_visualizer)
     # uncertainty_interval_size(altitude_to_visualizer)
@@ -72,7 +75,8 @@ def intermediate_result(altitudes, massif_names=None,
 def major_result():
     uncertainty_methods = [ConfidenceIntervalMethodFromExtremes.my_bayes,
                            ConfidenceIntervalMethodFromExtremes.ci_mle][1:]
-    massif_names = ['Beaufortain', 'Vercors']
+    # massif_names = ['Beaufortain', 'Vercors']
+    massif_names = None
     study_classes = paper_study_classes[:1]
     # model_subsets_for_uncertainty = [ModelSubsetForUncertainty.stationary_gumbel,
     #                                  ModelSubsetForUncertainty.stationary_gumbel_and_gev,