few more steps

etl/epc/Dataset.py

@@ -14,7 +14,33 @@ class TrainingDataset:
         self._feature_generation()
         self._drop_features()
         self._clean_dataframe()
-        self._clean_efficiency_variables(self.df)
+        self._clean_efficiency_variables()
+        self._null_validation(information="Clean Efficiency Variables")
+        self._process_and_prune()
+        self._clean_missing_values()
+        self._null_validation(information="Clean Missing Values")
+
+
+    def _clean_missing_values(self, ignore_cols=None):
+        missings = pd.isnull(self.df).sum()
+        missings = missings[missings > 0]
+
+        if ignore_cols:
+            missings = missings[~missings.index.isin(ignore_cols)]
+
+        for col in missings.index:
+            unique_values = self.df[col].unique()
+            if True in unique_values or False in unique_values:
+                self.df[col] = self.df[col].fillna(False)
+            if "none" in unique_values:
+                self.df[col] = self.df[col].fillna("none")
+            else:
+                self.df[col] = self.df[col].fillna("Unknown")
+
+
+    def _null_validation(self, information: str = ""):
+        if pd.isnull(self.df).sum().sum():
+            raise ValueError(f"Null values found in dataset, after step {information}")
 
     def _drop_features(self):
         """
@@ -30,8 +56,7 @@ class TrainingDataset:
         self.df["DAYS_TO_STARTING"] = self._calculate_days_to(self.df["LODGEMENT_DATE_STARTING"])
         self.df["DAYS_TO_ENDING"] = self._calculate_days_to(self.df["LODGEMENT_DATE_ENDING"])
 
-    @staticmethod
+    def _clean_efficiency_variables(self, df):
-    def _clean_efficiency_variables(df):
 
         """
         These is scope to clean this by the model per corresponding description.
@@ -43,20 +68,19 @@ class TrainingDataset:
         :return:
         """
 
-        missings = pd.isnull(df).sum()
+        missings = pd.isnull(self.df).sum()
         missings = missings[missings >= 1]
 
         if len(missings) == 0:
-            return df
+            return 
 
         # Make sure they are all efficiency columns
         if any(~missings.index.str.contains("ENERGY_EFF")):
             raise ValueError("Non efficiency columns are missing")
 
         for m in missings.index:
-            df[m] = df[m].fillna("NO_RATING")
+            self.df[m] = self.df[m].fillna("NO_RATING")
 
-        return df
 
     @staticmethod
     def _calculate_days_to(lodgement_date):

etl/epc/property_change_app.py

@@ -579,7 +579,7 @@ def app():
         from etl.epc.Dataset import TrainingDataset
         constituency_data = TrainingDataset(datasets=data_by_uprn)
 
-        data_by_urpn_df = pd.DataFrame(data_by_urpn)
+        # data_by_urpn_df = pd.DataFrame(data_by_urpn)
 
         # # TODO: can we move this into the epc record?
         # data_by_urpn_df["DAYS_TO_STARTING"] = DataProcessor.calculate_days_to(
@@ -592,7 +592,7 @@ def app():
 
         # data_by_urpn_df = data_by_urpn_df.drop(columns=["LODGEMENT_DATE_STARTING", "LODGEMENT_DATE_ENDING"])
 
-        data_by_urpn_df = DataProcessor.clean_efficiency_variables(data_by_urpn_df)
+        # data_by_urpn_df = DataProcessor.clean_efficiency_variables(data_by_urpn_df)
 
         # We look for key building fabric features that have changed from one EPC to the next.
         # if, for example, we see that a home has gone from being a cavity wall to a solid wall, we
@@ -600,8 +600,8 @@ def app():
         # is low
         # We also replace descriptions with their cleaned variants
 
-        if pd.isnull(data_by_urpn_df).sum().sum():
+        # if pd.isnull(data_by_urpn_df).sum().sum():
-            raise ValueError("Null values found in dataset")
+        #     raise ValueError("Null values found in dataset")
 
         data_by_urpn_df = process_and_prune_desriptions(data_by_urpn_df, cleaned_lookup)
 
@@ -617,10 +617,10 @@ def app():
         #       Those nulls should be False. clean_missings_after_description_process handles this but shouldn't
         #       need to
 
-        data_by_urpn_df = DataProcessor.clean_missings_after_description_process(data_by_urpn_df)
+        # data_by_urpn_df = DataProcessor.clean_missings_after_description_process(data_by_urpn_df)
 
-        if pd.isnull(data_by_urpn_df).sum().sum():
+        # if pd.isnull(data_by_urpn_df).sum().sum():
-            raise ValueError("Null values found in dataset after process_and_prune_desriptions")
+        #     raise ValueError("Null values found in dataset after process_and_prune_desriptions")
 
         dataset.append(data_by_urpn_df)