From 8b87442fcc95648e8ea2ad26b8a6203a724d1c3c Mon Sep 17 00:00:00 2001
From: marcopremier <marcopremier@meta.com>
Date: Wed, 6 Nov 2024 12:00:02 +0100
Subject: [PATCH] Remove debug prints statement

---
 python/src/robyn/modeling/pareto/pareto_optimizer.py | 12 +++++-------
 1 file changed, 5 insertions(+), 7 deletions(-)

diff --git a/python/src/robyn/modeling/pareto/pareto_optimizer.py b/python/src/robyn/modeling/pareto/pareto_optimizer.py
index 3a311420c..e04053b6d 100644
--- a/python/src/robyn/modeling/pareto/pareto_optimizer.py
+++ b/python/src/robyn/modeling/pareto/pareto_optimizer.py
@@ -378,7 +378,7 @@ def prepare_pareto_data(
         calibrated: bool,
     ) -> ParetoData:
         result_hyp_param = aggregated_data["result_hyp_param"]
-        print(f"~~~~~~~~~~ aggregated_data['x_decomp_agg'] PRE MERGE: {aggregated_data["x_decomp_agg"]}\n\n------------\n\nresult_hyp_param:{result_hyp_param}")
+
         # 1. Binding Pareto results
         aggregated_data["x_decomp_agg"] = pd.merge(
             aggregated_data["x_decomp_agg"],
@@ -386,7 +386,7 @@ def prepare_pareto_data(
             on="solID",
             how="left",
         )
-        print(f"~~~~~~~~~ 1")
+
         # Step 1: Collect decomp_spend_dist from each trial and add the trial number
         decomp_spend_dist = pd.concat(
             [
@@ -414,7 +414,7 @@ def prepare_pareto_data(
             on="solID",
             how="left",
         )
-        print(f"~~~~~~~~~ 2")
+
         # 3. Determining the number of Pareto fronts
         if self.model_outputs.hyper_fixed or len(result_hyp_param) == 1:
             pareto_fronts = 1
@@ -453,20 +453,18 @@ def prepare_pareto_data(
             pareto_fronts = int(auto_pareto["robynPareto"])
         # 5. Creating Pareto front vector
         pareto_fronts_vec = list(range(1, pareto_fronts + 1))
-        print(f"~~~~~~~~~ 3")
+
         # 6. Filtering data for selected Pareto fronts
         decomp_spend_dist_pareto = decomp_spend_dist[
             decomp_spend_dist["robynPareto"].isin(pareto_fronts_vec)
         ]
-        print(f"~~~~~~~~~ 3.1: \n\n")
         result_hyp_param_pareto = result_hyp_param[
             result_hyp_param["robynPareto"].isin(pareto_fronts_vec)
         ]
-        print(f"~~~~~~~~~ 3.2: {aggregated_data["x_decomp_agg"]}\n\n")
         x_decomp_agg_pareto = aggregated_data["x_decomp_agg"][
             aggregated_data["x_decomp_agg"]["robynPareto"].isin(pareto_fronts_vec)
         ]
-        print(f"~~~~~~~~~ 4")
+
         return ParetoData(
             decomp_spend_dist=decomp_spend_dist_pareto,
             result_hyp_param=result_hyp_param_pareto,