use kwargs only to call the initializer of super classes

Author: MalteKurz

doubleml_serverless/double_ml_iivm_aws_lambda.py

@@ -26,22 +26,22 @@ def __init__(self,
                  draw_sample_splitting=True,
                  apply_cross_fitting=True):
         DoubleMLIIVM.__init__(self,
-                              obj_dml_data,
-                              ml_g,
-                              ml_m,
-                              ml_r,
-                              n_folds,
-                              n_rep,
-                              score,
-                              subgroups,
-                              dml_procedure,
-                              trimming_rule,
-                              trimming_threshold,
-                              draw_sample_splitting,
-                              apply_cross_fitting)
+                              obj_dml_data=obj_dml_data,
+                              ml_g=ml_g,
+                              ml_m=ml_m,
+                              ml_r=ml_r,
+                              n_folds=n_folds,
+                              n_rep=n_rep,
+                              score=score,
+                              subgroups=subgroups,
+                              dml_procedure=dml_procedure,
+                              trimming_rule=trimming_rule,
+                              trimming_threshold=trimming_threshold,
+                              draw_sample_splitting=draw_sample_splitting,
+                              apply_cross_fitting=apply_cross_fitting)
         DoubleMLLambda.__init__(self,
-                                lambda_function_name,
-                                aws_region)
+                                lambda_function_name=lambda_function_name,
+                                aws_region=aws_region)
 
     def _ml_nuisance_aws_lambda(self, cv_params):
         assert self._dml_data.n_treat == 1

doubleml_serverless/double_ml_irm_aws_lambda.py

@@ -24,20 +24,20 @@ def __init__(self,
                  draw_sample_splitting=True,
                  apply_cross_fitting=True):
         DoubleMLIRM.__init__(self,
-                             obj_dml_data,
-                             ml_g,
-                             ml_m,
-                             n_folds,
-                             n_rep,
-                             score,
-                             dml_procedure,
-                             trimming_rule,
-                             trimming_threshold,
-                             draw_sample_splitting,
-                             apply_cross_fitting)
+                             obj_dml_data=obj_dml_data,
+                             ml_g=ml_g,
+                             ml_m=ml_m,
+                             n_folds=n_folds,
+                             n_rep=n_rep,
+                             score=score,
+                             dml_procedure=dml_procedure,
+                             trimming_rule=trimming_rule,
+                             trimming_threshold=trimming_threshold,
+                             draw_sample_splitting=draw_sample_splitting,
+                             apply_cross_fitting=apply_cross_fitting)
         DoubleMLLambda.__init__(self,
-                                lambda_function_name,
-                                aws_region)
+                                lambda_function_name=lambda_function_name,
+                                aws_region=aws_region)
 
     def _ml_nuisance_aws_lambda(self, cv_params):
         assert self._dml_data.n_treat == 1

doubleml_serverless/double_ml_pliv_aws_lambda.py

@@ -11,7 +11,7 @@ def __init__(self,
                  lambda_function_name,
                  aws_region,
                  obj_dml_data,
-                 ml_g,
+                 ml_l,
                  ml_m,
                  ml_r,
                  n_folds=5,
@@ -21,19 +21,19 @@ def __init__(self,
                  draw_sample_splitting=True,
                  apply_cross_fitting=True):
         DoubleMLPLIV.__init__(self,
-                              obj_dml_data,
-                              ml_g,
-                              ml_m,
-                              ml_r,
-                              n_folds,
-                              n_rep,
-                              score,
-                              dml_procedure,
-                              draw_sample_splitting,
-                              apply_cross_fitting)
+                              obj_dml_data=obj_dml_data,
+                              ml_l=ml_l,
+                              ml_m=ml_m,
+                              ml_r=ml_r,
+                              n_folds=n_folds,
+                              n_rep=n_rep,
+                              score=score,
+                              dml_procedure=dml_procedure,
+                              draw_sample_splitting=draw_sample_splitting,
+                              apply_cross_fitting=apply_cross_fitting)
         DoubleMLLambda.__init__(self,
-                                lambda_function_name,
-                                aws_region)
+                                lambda_function_name=lambda_function_name,
+                                aws_region=aws_region)
 
     def _ml_nuisance_aws_lambda(self, cv_params):
         assert self._dml_data.n_treat == 1

doubleml_serverless/double_ml_plr_aws_lambda.py

@@ -11,7 +11,7 @@ def __init__(self,
                  lambda_function_name,
                  aws_region,
                  obj_dml_data,
-                 ml_g,
+                 ml_l,
                  ml_m,
                  n_folds=5,
                  n_rep=1,
@@ -20,18 +20,18 @@ def __init__(self,
                  draw_sample_splitting=True,
                  apply_cross_fitting=True):
         DoubleMLPLR.__init__(self,
-                             obj_dml_data,
-                             ml_g,
-                             ml_m,
-                             n_folds,
-                             n_rep,
-                             score,
-                             dml_procedure,
-                             draw_sample_splitting,
-                             apply_cross_fitting)
+                             obj_dml_data=obj_dml_data,
+                             ml_l=ml_l,
+                             ml_m=ml_m,
+                             n_folds=n_folds,
+                             n_rep=n_rep,
+                             score=score,
+                             dml_procedure=dml_procedure,
+                             draw_sample_splitting=draw_sample_splitting,
+                             apply_cross_fitting=apply_cross_fitting)
         DoubleMLLambda.__init__(self,
-                                lambda_function_name,
-                                aws_region)
+                                lambda_function_name=lambda_function_name,
+                                aws_region=aws_region)
 
     def _ml_nuisance_aws_lambda(self, cv_params):
         assert self._dml_data.n_treat == 1