More Checks on input data

gettsim/checks.py

@@ -1,3 +1,36 @@
-def check_boolean(df, variable_list):
-    for variable in variable_list:
-        assert df[variable].dtype == bool
+def check_data(df):
+    bool_variables = [
+        "child",
+        "east",
+        "female",
+        "head",
+        "haskids",
+        "pkv",
+        "ineducation",
+        "eigentum",
+        "pensioner",
+    ]
+    for variable in bool_variables:
+        try:
+            assert df[variable].dtype == bool
+        except TypeError:
+            print(f"{variable} is not of type boolean.")
+
+    positive_vars = ["mietstufe", "wohnfl", "miete"]
+    for var in positive_vars:
+        try:
+            assert df[var].min() > 0
+        except ValueError:
+            print(f"{var} must be strictly positive.")
+
+    try:
+        assert df.notna().all().all()
+    except ValueError:
+        print("NaN value encountered in input data")
+
+    try:
+        assert (df.groupby("hid")["head"].sum() == 1).all()
+        assert (df.groupby("tu_id")["head_tu"].sum() == 1).all()
+    except ValueError:
+        print("There must be exactly one household head per household.")
+        print(df["hid"].first())

gettsim/tax_transfer.py

@@ -4,7 +4,7 @@
 from gettsim.benefits.kiz import kiz
 from gettsim.benefits.unterhaltsvorschuss import uhv
 from gettsim.benefits.wohngeld import wg
-from gettsim.checks import check_boolean
+from gettsim.checks import check_data
 from gettsim.incomes import disposable_income
 from gettsim.incomes import gross_income
 from gettsim.pensions import pensions
@@ -50,8 +50,8 @@ def tax_transfer(
     The 'sub' functions may take an argument 'ref', which might be used for small
      reforms that e.g. only differ in parameters or slightly change the calculation.
     """
-    bool_variables = ["child", "east"]
-    check_boolean(df, bool_variables)
+
+    check_data(df)
     # if hyporun is False:
     # df = uprate(df, datayear, settings['taxyear'], settings['MAIN_PATH'])
 

gettsim/tests/test_calculation_tax_transfer.py

@@ -5,7 +5,7 @@
 from gettsim.tax_transfer import calculate_tax_and_transfers
 
 
-YEARS = [2002, 2010, 2018, 2019]
+YEARS = [2002, 2010, 2012, 2013, 2014, 2018, 2019]
 
 
 @pytest.fixture(scope="module")

gettsim/tests/test_data/test_dfs_tax_transfer.csv

@@ -1,15 +1,15 @@
-hid,tu_id,pid,head_tu,head,adult_num,child0_18_num,hh_wealth,m_wage,east,age,selfemployed,haskids,m_self,m_pensions,pkv,m_wage_l1,months_ue,months_ue_l1,months_ue_l2,w_hours,child_num_tu,adult_num_tu,byear,exper,EP,child,pensioner,m_childcare,m_imputedrent,m_kapinc,m_vermiet,renteneintritt,zveranl,ineducation,handcap_degree,alleinerz,miete,wohnfl,eigentum,heizkost,cnstyr,mietstufe,m_transfers,year
-1,1,1,True,True,4,0,5500,300,False,25,False,False,0,0,False,1000,1,0,0,0,0,4,1958,7,6.2776,False,False,0,0,200,0,2061,False,False,0,False,400,50,False,80,3,3,50,2018
-1,1,2,False,False,4,0,5500,600,False,25,False,False,0,0,False,2000,1,0,0,0,0,4,1957,7,6.2776,False,False,0,0,200,1,2062,False,False,0,False,400,50,False,80,3,3,50,2018
-1,2,3,False,False,4,0,5500,900,False,25,False,False,0,0,False,3000,12,5,0,0,1,4,1982,7,6.2776,False,False,0,0,200,2,2063,False,False,0,False,400,50,False,80,3,3,50,2018
-1,2,4,False,False,4,0,5500,1200,True,25,False,True,0,0,False,4000,1,0,0,20,1,4,1970,7,6.2776,False,False,0,0,200,3,2064,False,False,0,False,400,50,False,80,3,3,50,2018
-2,3,5,True,True,4,0,5500,1500,True,25,False,True,0,0,False,7000,1,0,0,0,1,4,1952,7,6.2776,False,False,0,0,200,4,2065,False,False,0,False,400,50,False,80,3,3,50,2018
-2,3,6,False,False,4,0,5500,5000,True,25,False,True,0,0,False,2500,1,0,0,0,1,4,1985,7,6.2776,False,False,0,0,201,5,2066,False,False,0,False,400,50,False,80,3,3,50,2018
-2,4,7,False,False,4,0,5500,300,True,20,False,False,0,0,False,2300,1,0,0,0,0,4,1982,2,1.7936,False,False,0,0,202,6,2067,False,False,0,False,400,50,False,80,3,3,50,2010
-2,4,8,False,False,4,0,5500,600,True,20,False,False,0,0,False,1000,1,0,0,0,0,4,1971,2,1.7936,False,False,0,0,203,7,2068,False,False,0,False,400,50,False,80,3,3,50,2010
-3,5,9,True,True,1,0,5500,900,True,20,False,False,0,0,False,2000,1,0,0,0,0,1,1968,2,1.7936,False,False,0,0,204,8,2069,False,False,0,False,400,50,False,80,3,3,50,2010
-4,6,10,True,True,1,0,5500,1200,False,20,False,True,0,0,False,3000,12,5,0,0,1,1,1964,2,1.7936,False,False,0,0,205,9,2070,False,False,0,False,400,50,False,80,3,3,50,2010
-5,7,11,True,True,1,0,5500,1500,False,20,False,True,0,0,False,4000,1,0,0,20,1,1,1967,2,1.7936,False,False,0,0,206,10,2071,False,False,0,False,400,50,False,80,3,3,50,2010
-6,8,12,True,True,1,0,5500,8000,False,20,False,True,0,0,False,7000,1,0,0,0,1,1,1951,2,1.7936,False,False,0,0,207,11,2072,False,False,0,False,400,50,False,80,3,3,50,2010
-7,9,13,True,True,1,0,5500,700,False,20,False,False,0,0,False,2500,1,0,0,0,1,1,1960,2,1.7936,False,False,0,0,208,12,2073,False,False,0,False,400,50,False,80,3,3,50,2002
-8,10,14,True,True,1,0,5500,0,False,40,True,True,2500,0,False,2300,1,0,0,0,0,1,1959,22,19.7296,False,False,0,0,0,13,2074,False,False,0,False,400,50,False,80,3,3,50,2019
+hid,tu_id,pid,head_tu,female,head,adult_num,child0_18_num,hh_wealth,m_wage,east,age,selfemployed,haskids,m_self,m_pensions,pkv,m_wage_l1,months_ue,months_ue_l1,months_ue_l2,w_hours,child_num_tu,adult_num_tu,byear,exper,EP,child,pensioner,m_childcare,m_imputedrent,m_kapinc,m_vermiet,renteneintritt,zveranl,ineducation,handcap_degree,alleinerz,miete,wohnfl,eigentum,heizkost,cnstyr,mietstufe,m_transfers,year
+1,1,1,True,True,True,4,0,5500,300,False,25,False,False,0,0,False,1000,1,0,0,0,0,4,1958,7,6.2776,False,False,0,0,200,0,2061,False,False,0,False,400,50,False,80,3,3,50,2018
+1,1,2,False,True,False,4,0,5500,600,False,25,False,False,0,0,False,2000,1,0,0,0,0,4,1957,7,6.2776,False,False,0,0,200,1,2062,False,False,0,False,400,50,False,80,3,3,50,2018
+1,2,3,True,True,False,4,0,5500,900,False,25,False,False,0,0,False,3000,12,5,0,0,1,4,1982,7,6.2776,False,False,0,0,200,2,2063,False,False,0,False,400,50,False,80,3,3,50,2018
+1,2,4,False,True,False,4,0,5500,1200,True,25,False,True,0,0,False,4000,1,0,0,20,1,4,1970,7,6.2776,False,False,0,0,200,3,2064,False,False,0,False,400,50,False,80,3,3,50,2018
+2,3,5,True,True,True,4,0,5500,1500,True,25,False,True,0,0,False,7000,1,0,0,0,1,4,1952,7,6.2776,False,False,0,0,200,4,2065,False,False,0,False,400,50,False,80,3,3,50,2010
+2,3,6,False,True,False,4,0,5500,5000,True,25,False,True,0,0,False,2500,1,0,0,0,1,4,1985,7,6.2776,False,False,0,0,201,5,2066,False,False,0,False,400,50,False,80,3,3,50,2010
+2,4,7,True,True,False,4,0,5500,300,True,20,False,False,0,0,False,2300,1,0,0,0,0,4,1982,2,1.7936,False,False,0,0,202,6,2067,False,False,0,False,400,50,False,80,3,3,50,2010
+2,4,8,False,True,False,4,0,5500,600,True,20,False,False,0,0,False,1000,1,0,0,0,0,4,1971,2,1.7936,False,False,0,0,203,7,2068,False,False,0,False,400,50,False,80,3,3,50,2010
+3,5,9,True,True,True,1,0,5500,900,True,20,False,False,0,0,False,2000,1,0,0,0,0,1,1968,2,1.7936,False,False,0,0,204,8,2069,False,False,0,False,400,50,False,80,3,3,50,2012
+4,6,10,True,True,True,1,0,5500,1200,False,20,False,True,0,0,False,3000,12,5,0,0,1,1,1964,2,1.7936,False,False,0,0,205,9,2070,False,False,0,False,400,50,False,80,3,3,50,2012
+5,7,11,True,True,True,1,0,5500,1500,False,20,False,True,0,0,False,4000,1,0,0,20,1,1,1967,2,1.7936,False,False,0,0,206,10,2071,False,False,0,False,400,50,False,80,3,3,50,2013
+6,8,12,True,True,True,1,0,5500,8000,False,20,False,True,0,0,False,7000,1,0,0,0,1,1,1951,2,1.7936,False,False,0,0,207,11,2072,False,False,0,False,400,50,False,80,3,3,50,2014
+7,9,13,True,True,True,1,0,5500,700,False,20,False,False,0,0,False,2500,1,0,0,0,1,1,1960,2,1.7936,False,False,0,0,208,12,2073,False,False,0,False,400,50,False,80,3,3,50,2002
+8,10,14,True,True,True,1,0,5500,0,False,40,True,True,2500,0,False,2300,1,0,0,0,0,1,1959,22,19.7296,False,False,0,0,0,13,2074,False,False,0,False,400,50,False,80,3,3,50,2019