adding quantile regressor models for the residuals

src/sageworks/core/transforms/features_to_model/light_quant_regression/quant_regression.template

@@ -105,7 +105,29 @@ if __name__ == "__main__":
     result_df["mean"] = rmse_model.predict(X)
     result_df["prediction"] = result_df["mean"]
 
-    # Save the quantile and RMSE predictions to S3
+    # Now compute residuals on the prediction and compute quantiles on the residuals
+    result_df["residual"] = result_df[target] - result_df["prediction"]
+    result_df["residual_abs"] = result_df["residual"].abs()
+
+    # Change the target to the residual
+    y = result_df["residual"]
+
+    # Train models for each of the residual quantiles
+    for q in quantiles:
+        params = {
+            "objective": "reg:quantileerror",
+            "quantile_alpha": q,
+        }
+        model = xgb.XGBRegressor(**params)
+        model.fit(X, y)
+
+        # Convert quantile to string
+        q_str = f"qr_{int(q * 100):02}"
+
+        # Store the model
+        q_models[q_str] = model
+
+    # Save the target quantiles and residual quantiles to S3
     wr.s3.to_csv(
         result_df,
         path=f"{model_metrics_s3_path}/validation_predictions.csv",
@@ -121,7 +143,7 @@ if __name__ == "__main__":
     print(f"R2: {r2:.3f}")
     print(f"NumRows: {len(result_df)}")
 
-    # Now save the Quantile models to the standard place
+    # Now save the both the target quantile and residual quantiles models
     for name, model in q_models.items():
         model_path = os.path.join(args.model_dir, f"{name}.json")
         print(f"Saving model:  {model_path}")
@@ -150,7 +172,7 @@ def model_fn(model_dir) -> dict:
     # Load ALL the Quantile models from the model directory
     models = {}
     for file in os.listdir(model_dir):
-        if file.startswith("q_") and file.endswith(".json"):  # The Quantile models
+        if file.startswith("q") and file.endswith(".json"):  # The Quantile models
             # Load the model
             model_path = os.path.join(model_dir, file)
             print(f"Loading model: {model_path}")