Remove debug prints statement

facebookexperimental · Nov 6, 2024 · 8b87442 · 8b87442
1 parent c33772b
commit 8b87442
Showing 1 changed file with 5 additions and 7 deletions.
diff --git a/python/src/robyn/modeling/pareto/pareto_optimizer.py b/python/src/robyn/modeling/pareto/pareto_optimizer.py
@@ -378,15 +378,15 @@ 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"],
             result_hyp_param[["robynPareto", "solID"]],
             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,