add identifier column to datasets

modules/ml-pipeline/src/pipeline/2_build_model.py

@@ -6,7 +6,7 @@ Once we have the features, we build a model
 import os
 import yaml
 import pandas as pd
-from typing import Union
+from typing import Union, List
 from pathlib import Path
 from core.Logger import logger
 from core.interface.InterfaceMetrics import MLMetrics
@@ -31,6 +31,9 @@ generate_metrics_params = settings.generate_metrics
 
 model_type = build_model_params["model_type"]
 target = feature_process_params["feature_processor_config"]["target"]
+identifier_columns = feature_process_params["feature_processor_config"][
+    "identifier_columns"
+]
 model_save_location = build_model_params["model_save_filepath"]
 model_hyperparameters = build_model_params[model_type]
 train_filepath = prepare_data_params["output_train_filepath"]
@@ -62,6 +65,7 @@ def build_model(
     model: MLModel,
     metrics: MLMetrics,
     target: str,
+    identifier_columns: List[str],
     model_save_location: str,
     model_hyperparameters: dict,
     fit_metrics_filepath: str,
@@ -89,18 +93,20 @@ def build_model(
     logger.info("--- Training model ---")
     logger.info("----------------------")
 
+    model_train_data = train_data.drop(columns=identifier_columns)
+
     model.train_model(
-        data=train_data, target=target, model_hyperparameters=model_hyperparameters
+        data=model_train_data,
+        target=target,
+        model_hyperparameters=model_hyperparameters,
     )
 
     logger.info("----------------------------------")
     logger.info("--- Generating fit predictions ---")
     logger.info("----------------------------------")
 
-    prediction_data = train_data.drop(columns=target)
-
     fit_predictions = model.predict(
-        data=prediction_data, post_prediction_logic=post_prediction_logic
+        data=train_data, post_prediction_logic=post_prediction_logic
     )
 
     logger.info("------------------------------")
@@ -142,6 +148,7 @@ if __name__ == "__main__":
         model=model,
         metrics=metrics,
         target=target,
+        identifier_columns=identifier_columns,
         model_save_location=model_save_location,
         model_hyperparameters=model_hyperparameters,
         train_filepath=train_filepath,

modules/ml-pipeline/src/pipeline/configs/settings.yaml

@@ -32,7 +32,8 @@ default:
       subsample_amount: null
       subsample_seed: 0
       target: SAP_ENDING
-      drop_columns: ["UPRN", "HEAT_DEMAND_CHANGE", "CARBON_CHANGE", "RDSAP_CHANGE", "HEAT_DEMAND_ENDING", "CARBON_ENDING"]
+      identifier_columns: ["UPRN"]
+      drop_columns: ["HEAT_DEMAND_CHANGE", "CARBON_CHANGE", "RDSAP_CHANGE", "HEAT_DEMAND_ENDING", "CARBON_ENDING"]
       # retain_features: ["SAP_STARTING", "TOTAL_FLOOR_AREA_DIFF"]
       retain_features: null