adding seed in column

owkin · Jun 15, 2022 · ed66b87 · ed66b87
1 parent 6366c64
commit ed66b87
Showing 1 changed file with 26 additions and 13 deletions.
diff --git a/flamby/benchmarks/fed_benchmark.py b/flamby/benchmarks/fed_benchmark.py
@@ -86,8 +86,12 @@ def main(args_cli):
     strategy_specific_hp_dicts = get_strategies(
         config, learning_rate=LR, args=vars(args_cli)
     )
-    pooled_hyperparameters = {"optimizer_class": Optimizer, "learning_rate": LR}
-    main_columns_names = ["Test", "Method", "Metric"]
+    pooled_hyperparameters = {
+        "optimizer_class": Optimizer,
+        "learning_rate": LR,
+        "seed": args_cli.seed,
+    }
+    main_columns_names = ["Test", "Method", "Metric", "seed"]
 
     # We might need to dynamically add additional parameters to the csv columns
     hp_additional_args = []
@@ -99,7 +103,7 @@ def main(args_cli):
             if arg_names not in hp_additional_args
         ]
     # column names used for the results file
-    columns_names = main_columns_names + hp_additional_args
+    columns_names = list(set(main_columns_names + hp_additional_args))
 
     evaluate_func, batch_size_test, compute_ensemble_perf = set_dataset_specific_config(
         dataset_name, compute_ensemble_perf=True
@@ -159,7 +163,9 @@ def main(args_cli):
     # We can now proceed to the trainings
     # Pooled training
     # We check if we have already the results for pooled
-    index_of_interest = df.loc[df["Method"] == "Pooled Training"].index
+    index_of_interest = df.loc[
+        (df["Method"] == "Pooled Training") & (df["seed"] == args_cli.seed)
+    ].index
 
     # There is no use in running the experiment if it is already found
     if (len(index_of_interest) < (NUM_CLIENTS + 1)) and do_baselines["Pooled"]:
@@ -207,13 +213,17 @@ def main(args_cli):
             pooled=True,
         )
 
-    # We check if we have the results for local and possibly ensemble as well
-    index_of_interest = df.loc[df["Method"] == "Local 0"].index
+    # We check if we have the results for local trainings and possibly ensemble as well
+    index_of_interest = df.loc[
+        (df["Method"] == "Local 0") & (df["seed"] == args_cli.seed)
+    ].index
     for i in range(1, NUM_CLIENTS):
         index_of_interest = index_of_interest.union(
-            df.loc[df["Method"] == f"Local {i}"].index
+            df.loc[(df["Method"] == f"Local {i}") & (df["seed"] == args_cli.seed)].index
         )
-    index_of_interest = index_of_interest.union(df.loc[df["Method"] == "Ensemble"].index)
+    index_of_interest = index_of_interest.union(
+        df.loc[(df["Method"] == "Ensemble") & (df["seed"] == args_cli.seed)].index
+    )
 
     if len(index_of_interest) < nb_local_and_ensemble_xps:
         # The fact that we are here means some local experiments are missing or
@@ -224,7 +234,9 @@ def main(args_cli):
         pooled_y_pred_dicts = {}
 
         for i in range(NUM_CLIENTS):
-            index_of_interest = df.loc[df["Method"] == f"Local {i}"].index
+            index_of_interest = df.loc[
+                (df["Method"] == f"Local {i}") & (df["seed"] == args_cli.seed)
+            ].index
             # We do the experiments only if results are not found or we need
             # ensemble performances AND this experiment is planned.
             # i.e. we allow to not do anything else if the user specify
@@ -241,8 +253,8 @@ def main(args_cli):
                     training_dls[i],
                     use_gpu,
                     method_name,
-                    Optimizer,
-                    LR,
+                    pooled_hyperparameters["optimizer_class"],
+                    pooled_hyperparameters["learning_rate"],
                     BaselineLoss,
                     NUM_EPOCHS_POOLED,
                 )
@@ -259,15 +271,15 @@ def main(args_cli):
                 df = fill_df_with_xp_results(
                     df,
                     perf_dict,
-                    {"learning_rate": LR, "optimizer_class": Optimizer},
+                    pooled_hyperparameters,
                     method_name,
                     columns_names,
                     results_file,
                 )
                 df = fill_df_with_xp_results(
                     df,
                     pooled_perf_dict,
-                    {"learning_rate": LR, "optimizer_class": Optimizer},
+                    pooled_hyperparameters,
                     method_name,
                     columns_names,
                     results_file,
@@ -336,6 +348,7 @@ def main(args_cli):
                         hyperparameters[k] = args[k]
                     else:
                         hyperparameters[k] = np.nan
+                hyperparameters["seed"] = args_cli.seed
 
                 index_of_interest = find_xps_in_df(
                     df, hyperparameters, sname, num_updates