[SIMULATION] add draft

experiment/simulation/draft_main.py

+
+
+
+if __name__ == '__main__':
+    # Parameters
+    scenarios = []
+    nb_obs_list = []
+    nb_fit = 1000
+
+    # Load the object that will handle the simulation
+    simu = Simulations(nb_fit, scenarios, nb_obs_list)
+
+    # Fit many estimators to this simulation
+    estimator_types = []
+    for estimator_type in estimator_types:
+        simu.fit(estimator_type)
+
+    # Comparison of the diverse estimator
+
+    # Compare all the estimator on a global graph (one graph per scenario)
+    # On each graph the X axis should be the number of obs
+    # the Y graph should the error
+    simu.visualize_mean_test_error_graph(estimator_types, scenarios, nb_obs_list)
+    # the other possible view, is to have one graph per number of observations
+    # on the X axis should the name of the different estimator
+    # on the y axis their error
+
+
+    # Plot the same graph for the train/test error
+    # For a single scenario, and a single obs (we give a plot detailing all the estimation steps that enabled to get
+    # the result)
+    simu.visualize_comparison_graph(estimator_types, scenario, nb_obs)
+
+    # Analyse the result of a single estimator
+
+    # Or all the result could be recorded in a matrix, with scenario as line, and nb_observaitons as columns
+    # with the mean value (and the std in parenthesis)
+    # (on the border on this matrix we should have the mean value)
+    # for example, the first columns should be the mean of the other column for the same line
+    simu.visualize_mean_test_error_matrix(estimator_type, scenarios, nb_obs_list)
+
+
+    #
+    simu.visualize
+
+
+

experiment/simulation/lin_space2_simulation.py

@@ -15,15 +15,20 @@ class LinSpace5Simulation(AbstractSimulation):
     def __init__(self, nb_fit=1):
         super().__init__(nb_fit)
         # Simulation parameters
+        # Number of observations
         self.nb_obs = 60
-        self.coordinates = LinSpaceSpatialCoordinates.from_nb_points(nb_points=100, train_split_ratio=0.75)
-        # MarginModel Linear with respect to the shape (from 0.01 to 0.02)
+        # 1 dimensional spatial coordinates (separated in train split and test split)
+        self.coordinates = LinSpaceSpatialCoordinates.from_nb_points(nb_points=100,
+                                                                     train_split_ratio=0.75)
+        # MarginModel Constant for simulation
         params_sample = {
             (GevParams.GEV_LOC, 0): 1.0,
             (GevParams.GEV_SHAPE, 0): 1.0,
             (GevParams.GEV_SCALE, 0): 1.0,
         }
-        self.margin_model = ConstantMarginModel(coordinates=self.coordinates, params_sample=params_sample)
+        self.margin_model = ConstantMarginModel(coordinates=self.coordinates,
+                                                params_sample=params_sample)
+        # MaxStable Model for simulation
         self.max_stable_model = Smith()
 
     def dump(self):
@@ -37,6 +42,6 @@ if __name__ == '__main__':
     simu = LinSpace5Simulation(nb_fit=10)
     simu.dump()
     estimators_class = MARGIN_ESTIMATORS_FOR_SIMULATION + FULL_ESTIMATORS_FOR_SIMULATION
-    for estimator_class in estimators_class[:]:
-        simu.fit(estimator_class, show=False)
+    # for estimator_class in estimators_class[:]:
+    #     simu.fit(estimator_class, show=False)
     simu.visualize_comparison_graph()