Update

Author: Piwington

statistical_methods_library/imputation/engine.py

@@ -450,7 +450,7 @@ def calculate_weighted_link(link_name):
                     ["period", "grouping"],
                 )
             )
-
+        
         return df
 
     # Caching for both imputed and unimputed data.

statistical_methods_library/imputation/ratio_calculators.py

@@ -276,8 +276,7 @@ def upper_bound(c):
         )
 
     growth_df = df.select(
-        "ref", "period", "grouping", "link_inclusion_previous", "link_inclusion_next",
-        "link_inclusion_current", *growth_additional_outputs.keys()
+        "ref", "period", "grouping", *growth_additional_outputs.keys()
     )
 
     return [

tests/fixture_data/imputation/mean_of_ratios/methodology_scenarios/49_R_R_FI-BI_R_R_50_weight_previous_month_output.csv

@@ -1,27 +1,27 @@
 identifier,date,group,other,growth_forward,growth_backward,forward,backward,construction,unweighted_forward,unweighted_backward,unweighted_construction,output,marker,count_forward,count_backward,count_construction,default_forward,default_backward,default_construction
 110001,201912,100,89,,,,,,1,1.5274,50.3694,0,BI,,,,,,
-110001,202001,100,89,,1.036787797,1,2.09204015,76.03985875,1,2.656680299,101.710317,9244,R,0,4,4,true,false,false
+110001,202001,100,89,,1.036787797,1,2.09204015,76.03985875,1,2.65668,101.710317,9244,R,0,4,4,true,false,false
 110001,202002,100,89,0.964517525,1.43945754,0.885925,1.894176015,88.31349209,0.771850199,1.13167173,74.91666667,8916,R,4,3,4,false,false,false
 110001,202003,100,89,0.694706146,,0.870040426,1.065836,72.58901516,0.968230653,1,70.26136364,6194,R,3,0,3,false,true,false
-110002,202001,100,83,,0.817550398,1,2.09204015,76.03985875,1,2.656680299,101.710317,4826,R,0,4,4,true,false,false
+110002,202001,100,83,,0.817550398,1,2.09204015,76.03985875,1,2.65668,101.710317,4826,R,0,4,4,true,false,false
 110002,202002,100,83,1.223166183,1.244570947,0.885925,1.894176015,88.31349209,0.771850199,1.13167173,74.91666667,5903,R,4,3,4,false,false,false
 110002,202003,100,83,0.803489751,,0.870040426,1.065836,72.58901516,0.968230653,1,70.26136364,4743,R,3,0,3,false,true,false
-110003,202001,100,4,,7.466535433,1,2.09204015,76.03985875,1,2.656680299,101.710317,7586,R,0,4,4,true,false,false
+110003,202001,100,4,,7.466535433,1,2.09204015,76.03985875,1,2.65668,101.710317,7586,R,0,4,4,true,false,false
 110003,202002,100,4,0.133930925,0.710986704,0.885925,1.894176015,88.31349209,0.771850199,1.13167173,74.91666667,1016,R,4,3,4,false,false,false
 110003,202003,100,4,1.406496063,,0.870040426,1.065836,72.58901516,0.968230653,1,70.26136364,1429,R,3,0,3,false,true,false
-110004,202001,100,76,,1.305847569,1,2.09204015,76.03985875,1,2.656680299,101.710317,3975,R,0,4,4,true,false,false
+110004,202001,100,76,,1.305847569,1,2.09204015,76.03985875,1,2.65668,101.710317,3975,R,0,4,4,true,false,false
 110004,202002,100,76,0.765786164,,0.885925,1.894176015,88.31349209,0.771850199,1.13167173,74.91666667,3044,R,4,3,4,false,false,false
 110004,202003,100,76,,,0.870040426,1.065836,72.58901516,0.968230653,1,70.26136364,2648.403058,FIR,3,0,3,false,true,false
 110005,201912,200,27,,,,,,1,2.994350985,107.48,0,BI,,,,,,
-110005,202001,200,27,,0.743586089,1,2.642597676,144.865,1,2.290844366,182.25,5217,R,0,3,3,true,false,false
+110005,202001,200,27,,0.743586089,1,2.642597676,144.865,1,2.290844,182.25,5217,R,0,3,3,true,false,false
 110005,202002,200,27,1.344834196,0.70583501,0.99724,1.562080758,166.2624046,0.994479964,0.833317149,150.2748092,7016,R,3,4,4,false,false,false
 110005,202003,200,27,1.416761688,,1.127130141,0.916659,166.2557252,1.259780318,1,182.2366412,9940,R,4,0,4,false,true,false
-110006,202001,200,42,,0.68736285,1,2.642597676,144.865,1,2.290844366,182.25,5325,R,0,3,3,true,false,false
+110006,202001,200,42,,0.68736285,1,2.642597676,144.865,1,2.290844,182.25,5325,R,0,3,3,true,false,false
 110006,202002,200,42,1.454835681,1.158863126,0.99724,1.562080758,166.2624046,0.994479964,0.833317149,150.2748092,7747,R,3,4,4,false,false,false
 110006,202003,200,42,0.862914677,,1.127130141,0.916659,166.2557252,1.259780318,1,182.2366412,6685,R,4,0,4,false,true,false
-110007,202001,200,19,,5.441584158,1,2.642597676,144.865,1,2.290844366,182.25,5496,R,0,3,3,true,false,false
+110007,202001,200,19,,5.441584158,1,2.642597676,144.865,1,2.290844,182.25,5496,R,0,3,3,true,false,false
 110007,202002,200,19,0.183770015,0.817813765,0.99724,1.562080758,166.2624046,0.994479964,0.833317149,150.2748092,1010,R,3,4,4,false,false,false
 110007,202003,200,19,1.222772277,,1.127130141,0.916659,166.2557252,1.259780318,1,182.2366412,1235,R,4,0,4,false,true,false
-110008,202001,200,43,,,1,2.642597676,144.865,1,2.290844366,182.25,10340.484733,BI,0,3,3,true,false,false
+110008,202001,200,43,,,1,2.642597676,144.865,1,2.290844,182.25,10340.484733,BI,0,3,3,true,false,false
 110008,202002,200,43,,0.650756694,0.99724,1.562080758,166.2624046,0.994479964,0.833317149,150.2748092,3913,R,3,4,4,false,false,false
 110008,202003,200,43,1.53667263,,1.127130141,0.916659,166.2557252,1.259780318,1,182.2366412,6013,R,4,0,4,false,true,false

tests/imputation/test_mean_of_ratios.py

@@ -217,8 +217,7 @@ def test_calculations(fxt_load_test_csv, scenario_type, scenario):
     scenario_expected_output = scenario_expected_output.filter(
         col(period_col) >= imputation_kwargs["starting_period"]
     )
-    scenario_input.show(100)
-    back_data_df.show(100)
+
     # We need to drop our grouping and auxiliary columns from our output now
     # we've potentially set up our back data as these must not come out of
     # imputation.