Relevant weights if not all contexts run equally

nevergrad/benchmark/plotting.py

@@ -603,7 +603,21 @@ def make_data(df: pd.DataFrame, normalized_loss: bool = False) -> tp.Dict[str, t
                 ["optimizer_name", "budget", "loss"] + (["pseudotime"] if "pseudotime" in df.columns else []),
             ]
         )
-        groupeddf = df.groupby(["optimizer_name", "budget"])
+        # We first aggregate equivalent rows. The only point of this is that we want all contexts to have the same
+        # weight, in e.g. xpresults_all.png, even if not all contexts have been run the same number of times.
+        descriptors = sorted(
+            set(df.columns)
+            - {
+                "pseudotime",
+                "time",
+                "elapsed_time",
+                "elapsed_budget",
+                "loss",
+                "seed",
+            }
+        )
+        compact_df = df.groupby(list(descriptors)).mean()  # We first aggregate equal contexts.
+        groupeddf = compact_df.groupby(["optimizer_name", "budget"])
         means = groupeddf.mean()
         stds = groupeddf.std()
         optim_vals: tp.Dict[str, tp.Dict[str, np.ndarray]] = {}