refactor near complete

backend/app/plan/router.py

@@ -145,6 +145,10 @@ async def trigger_plan(body: PlanTriggerRequest):
 
             recommendations[p.id] = property_recommendations
 
+            from etl.epc.Pipeline import EPCPipeline
+            from etl.epc.DataProcessor import EPCDataProcessor
+            newdata_pipeline = EPCPipeline(epc_data_processor=EPCDataProcessor(data=p.get_model_data(), run_mode="newdata"), run_mode="newdata")
+
             # TODO: p.get_model_data() -> EPCRecord
 
             # Finally, we'll prepare data for predicting the impact on SAP

etl/epc/DataProcessor.py

@@ -83,7 +83,7 @@ class EPCDataProcessor:
             raise ValueError("Run mode must be either training or newdata")
         self.run_mode = run_mode if not violation_mode else "newdata"
     
-    def prepare_data(self, filepath: Path | str) -> None:
+    def prepare_data(self, filepath: Path | str | None = None) -> None:
         """
         Given the run mode, we apply the relevant pipeline steps
         Ignore step is used to highlight which steps are not needed in newdata

etl/epc/Dataset.py

@@ -486,19 +486,19 @@ class TrainingDataset(BaseDataset):
             pd.to_datetime(lodgement_date) - pd.to_datetime(EARLIEST_EPC_DATE)
         ).dt.days
 
-    def __add__(self, other) -> "TrainingDataset":
+    # def __add__(self, other) -> "TrainingDataset":
-        if not isinstance(other, TrainingDataset):
+    #     if not isinstance(other, TrainingDataset):
-            raise TypeError("Addition can only be performed with another instance of TrainingDataset")
+    #         raise TypeError("Addition can only be performed with another instance of TrainingDataset")
-        return TrainingDataset(self.datasets + other.datasets)
+    #     return TrainingDataset(self.datasets + other.datasets)
         
-    def __radd__(self, other):
+    # def __radd__(self, other):
-        """
+    #     """
-        Required for sum() to work
+    #     Required for sum() to work
-        """
+    #     """
-        if isinstance(other, int):
+    #     if isinstance(other, int):
-            return self
+    #         return self
-        else:
+    #     else:
-            return self.__add__(other)
+    #         return self.__add__(other)
 
 class NewDataset(BaseDataset):
     """
@@ -506,7 +506,7 @@ class NewDataset(BaseDataset):
     """
 
     def __init__(self, datasets: List[EPCDifferenceRecord]) -> None:
-        self.pipeline_steps = self.pipeline_factory("newdata")
+        # self.pipeline_steps = self.pipeline_factory("newdata")
         self.datasets = datasets
 
     def __add__(self, other) -> "NewDataset":

etl/epc/Pipeline.py

@@ -66,8 +66,8 @@ class EPCPipeline:
 
     def __init__(
             self, 
-            directories: List[Path], 
             epc_data_processor: EPCDataProcessor, 
+            directories: List[Path] | None = None, 
             run_mode="training", 
             epc_local_file="certificates.csv", 
             epc_bucket_name="retrofit-data-dev", 
@@ -102,6 +102,29 @@ class EPCPipeline:
         """
         Entrypoint to run the pipeline
         """
+        if self.run_mode == "training":
+            self.run_training_dataset_pipeline()
+        elif self.run_mode == "newdata":
+            self.run_newdata_dataset_pipeline()
+        else:
+            raise ValueError("Run mode defined needs to be in 'training' or 'newdata'")
+        
+    def run_newdata_dataset_pipeline(self):
+        """
+        Main function to run the newdata pipeline
+        """
+        self.epc_data_processor.prepare_data()
+        data = self.epc_data_processor.data
+
+        epc_records = [EPCRecord(**x, run_mode="newdata") for x in data.to_dict(orient='records')]
+        
+    def run_training_dataset_pipeline(self):
+        """
+        Main function to run the training dataset generation pipeline
+        """
+        if self.directories is None:
+            raise ValueError("Directories not specified - Unable to run Training pipeline")
+        
         for directory in self.directories:
             self.process_directory(directory)
 
@@ -169,7 +192,7 @@ class EPCPipeline:
         variable_data = property_data[VARIABLE_DATA_FEATURES]
 
         uprn = str(uprn)
-        epc_records = [EPCRecord(uprn, **x) for x in variable_data.to_dict(orient='records')]
+        epc_records = [EPCRecord(uprn, **x, run_mode="training") for x in variable_data.to_dict(orient='records')]
 
         # TODO: We want to be able to provide value for the u values in the main pipeline so this will need to be part of the EPCRecord
 

etl/epc/Record.py

@@ -5,7 +5,7 @@ from etl.epc.ValidationConfiguration import (
     EPCDifferenceRecordValidationConfiguration, 
     EPCDifferenceRecordFixedDataValidationConfiguration
 )
-from typing import Union, List
+from typing import Any, Union, List
 from etl.epc.settings import (
     RDSAP_RESPONSE,
     HEAT_DEMAND_RESPONSE,
@@ -242,7 +242,7 @@ class EPCRecord:
         
         return self.__dict__[RDSAP_RESPONSE] <= other.__dict__[RDSAP_RESPONSE]
         
-    def get(self, key: Union[str, List[str]], return_asdict: bool = False, key_suffix: str = None):
+    def get(self, key: Union[str, List[str]], return_asdict: bool = False, key_suffix: str | None = None) -> Any:
         """
         This method will return the value of the key
         """