Repeats

Author: dellaert

timing/README.md

@@ -24,12 +24,14 @@ Run from `build/`:
 ```bash
 ./timing/timeBayesTreeCovariance \
   --datasets w100.graph,w10000.graph,w20000.txt \
+  --repeats 10 \
   --output-dir ../timing/results/bayes_tree_covariance
 ```
 
 This writes:
 
 - `timing/results/bayes_tree_covariance/raw.csv`
+- `timing/results/bayes_tree_covariance/per_query.csv`
 - `timing/results/bayes_tree_covariance/summary.csv`
 
 The generated CSVs now include the small-query families used to benchmark the
@@ -80,6 +82,14 @@ This generates:
 
 - The benchmark timings measure covariance-query work after optimization and
   linearization.
+- Each distinct query is run once as an untimed warmup and then `--repeats`
+  times as measured repetitions.
+- `raw.csv` stores one row per measured repetition.
+- `per_query.csv` stores one row per distinct query, aggregating repeated
+  timings by per-query means.
+- `summary.csv` stores one row per query family bucket, aggregating the
+  per-query means and structural statistics by medians across the sampled
+  queries.
 - The `results-smallq.pdf` figure summarizes the `Q = 1` and `Q = 2` query
   families; the larger performance figures focus on `Q > 2`, where Steiner
   localization changes the asymptotic behavior.

timing/plot_bayes_tree_covariance.py

@@ -7,7 +7,7 @@
 See LICENSE for the license information
 
 Generate paper-ready plots for the Bayes-tree covariance benchmarks.
-Author: Frank Dellaert
+Author: Codex 5.4, prompted by Frank Dellaert
 """
 
 import argparse
@@ -35,9 +35,10 @@ def to_float(rows, fields):
             row[field] = float(row[field])
         row["query_size"] = int(row["query_size"])
         row["queries"] = int(row["queries"])
-        row["support_cliques"] = int(row["support_cliques"])
-        row["compressed_cliques"] = int(row["compressed_cliques"])
-        row["reduced_state_dim"] = int(row["reduced_state_dim"])
+        row["repeats"] = int(row["repeats"])
+        row["support_cliques"] = float(row["support_cliques"])
+        row["compressed_cliques"] = float(row["compressed_cliques"])
+        row["reduced_state_dim"] = float(row["reduced_state_dim"])
     return rows
 
 
@@ -224,7 +225,7 @@ def plot_small_queries(rows, output_path):
                 "w20000\nMETIS",
             ],
         )
-        ax.set_ylabel("Median query time (ms)")
+        ax.set_ylabel("Median per-query mean time (ms)")
         ax.set_yscale("log")
         ax.grid(True, axis="y", alpha=0.3)
     handles, labels = axes[0].get_legend_handles_labels()
@@ -301,7 +302,7 @@ def plot_ordering(rows, output_path):
         ax.set_title(dataset_label(dataset))
         ax.set_xlabel("Local-window query size")
         ax.set_xticks(query_sizes, [str(size) for size in query_sizes])
-        ax.set_ylabel("Median query time (ms)")
+        ax.set_ylabel("Median per-query mean time (ms)")
         ax.set_yscale("log")
         ax.grid(True, axis="y", alpha=0.3)
     handles, labels = axes[0].get_legend_handles_labels()
@@ -349,7 +350,7 @@ def plot_structure(rows, output_path):
         )
         ax.set_title(dataset_label(dataset))
         ax.set_xlabel("Query size")
-        ax.set_ylabel("Count / dimension")
+        ax.set_ylabel("Median count / dimension")
         ax.grid(True, alpha=0.3)
     handles, labels = axes[0].get_legend_handles_labels()
     figure.legend(handles, labels, loc="upper center", ncol=3, frameon=False)
@@ -767,9 +768,12 @@ def main():
             load_rows(input_path),
             [
                 "median_total_ms",
-                "total_total_ms",
+                "sum_query_mean_total_ms",
                 "median_reduction_ms",
                 "median_extraction_ms",
+                "support_cliques",
+                "compressed_cliques",
+                "reduced_state_dim",
             ],
         )
     )