[contrasting] fix polynomial_margin_model.py creation. extend the form_dict...

[contrasting] fix polynomial_margin_model.py creation. extend the form_dict for spatio temporal models.

extreme_fit/distribution/gev/main_fevd_mle_two_covariates.R

@@ -27,7 +27,7 @@ coord = data.frame(coord, stringsAsFactors = TRUE)
 # res = fevd_fixed(x_gev, data=coord, method='MLE', verbose=TRUE, use.phi=FALSE)
 # res = fevd_fixed(x_gev, data=coord, location.fun= ~T, scale.fun= ~T, method='MLE', type="GEV", verbose=FALSE, use.phi=FALSE)
 # res = fevd_fixed(x_gev, data=coord, location.fun= ~sin(X) + cos(T), method='MLE', type="GEV", verbose=FALSE, use.phi=FALSE)
-res = fevd_fixed(x_gev, data=coord, location.fun= ~T * X + X +T , method='MLE', type="GEV", verbose=FALSE, use.phi=FALSE)
+res = fevd_fixed(x_gev, data=coord, location.fun= ~poly(X, 1, raw = TRUE) + 1 , method='MLE', type="GEV", verbose=FALSE, use.phi=FALSE)
 print(res)
 
 # Some display for the results

extreme_fit/model/margin_model/linear_margin_model/abstract_temporal_linear_margin_model.py

@@ -119,8 +119,6 @@ class AbstractTemporalLinearMarginModel(LinearMarginModel):
             df = df_coordinates_temp
         else:
             df = pd.concat([df_coordinates_spat, df_coordinates_temp], axis=1)
-            print(df.shape)
-            print(df.head())
         y = get_coord_df(df)
 
         # Disable the use of log sigma parametrization

extreme_fit/model/margin_model/polynomial_margin_model/polynomial_margin_model.py

@@ -57,7 +57,7 @@ class PolynomialMarginModel(AbstractTemporalLinearMarginModel):
             for dim, max_degree in param_name_to_list_dim_and_degree.get(param_name, []):
                 degree_to_coef = {}
                 for (param_name_loop, dim_loop, degree), coef in param_name_and_dim_and_degree_to_default_coef.items():
-                    if param_name == param_name_loop and dim == dim_loop:
+                    if param_name == param_name_loop and dim == dim_loop and degree <= max_degree:
                         degree_to_coef[degree] = coef
                 # print(degree_to_coef, param_name)
                 # if len(degree_to_coef) == 0:

extreme_fit/model/utils.py

@@ -90,7 +90,8 @@ def safe_run_r_estimator(function, data=None, start_dict=None, max_ratio_between
             for _ in range(nb_tries_for_start_value):
                 parameters['start'] = r.list(**start_dict)
                 try:
-                    return _safe_run_r_estimator(function, data, max_ratio_between_two_extremes_values, maxit, **parameters)
+                    return _safe_run_r_estimator(function, data, max_ratio_between_two_extremes_values, maxit,
+                                                 **parameters)
                 except Exception:
                     continue
         else:
@@ -98,7 +99,7 @@ def safe_run_r_estimator(function, data=None, start_dict=None, max_ratio_between
 
 
 def _safe_run_r_estimator(function, data=None, max_ratio_between_two_extremes_values=10, maxit=1000000,
-                         **parameters) -> robjects.ListVector:
+                          **parameters) -> robjects.ListVector:
     if OptimizationConstants.USE_MAXIT:
         # Add optimization parameters
         optim_dict = {'maxit': maxit}
@@ -170,17 +171,21 @@ def get_margin_formula_spatial_extreme(fit_marge_form_dict) -> Dict:
     return margin_formula
 
 
-def old_coef_name_to_new_coef_name():
+def new_coef_name_to_old_coef_names():
     return {
-        'temp.form.loc': 'location.fun',
-        'temp.form.scale': 'scale.fun',
-        'temp.form.shape': 'shape.fun',
+        'location.fun': ['loc.form', 'temp.form.loc'],
+        'scale.fun': ['scale.form', 'temp.form.scale'],
+        'shape.fun': ['shape.form', 'temp.form.shape'],
     }
 
 
 def get_margin_formula_extremes(fit_marge_form_dict) -> Dict:
-    d = old_coef_name_to_new_coef_name()
-    form_dict = {d[k]: ' '.join(v.split()[1:]) if v != 'NULL' else ' ~1' for k, v in fit_marge_form_dict.items()}
+    v_to_str = lambda v: ' '.join(v.split()[2:]) if v != 'NULL' else ' 1'
+    form_dict = {
+        k: '~' + ' + '.join(
+            [v_to_str(fit_marge_form_dict[e]) for e in l if e in fit_marge_form_dict])
+        for k, l in new_coef_name_to_old_coef_names().items()
+    }
     return {k: robjects.Formula(v) for k, v in form_dict.items()}
 
 # def conversion_to_FloatVector(data):