Benchmark: Move relative statistics to benchmark script

benchmark/benchmark.py

@@ -31,6 +31,9 @@ import glob
 from docopt import docopt
 from numpy import median
 from pathlib import Path
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
 
 from miplearn import (
     LearningSolver,
@@ -132,16 +135,19 @@ def test_ml():
 
 
 def charts():
-    import matplotlib.pyplot as plt
-    import seaborn as sns
-
     sns.set_style("whitegrid")
     sns.set_palette("Blues_r")
-    benchmark = BenchmarkRunner({})
-    benchmark.load_results(f"{basepath}/benchmark_baseline.csv")
-    benchmark.load_results(f"{basepath}/benchmark_ml.csv")
-    results = benchmark.raw_results()
-    results["Gap (%)"] = results["Gap"] * 100.0
+
+    csv_files = [
+        f"{basepath}/benchmark_baseline.csv",
+        f"{basepath}/benchmark_ml.csv",
+    ]
+    results = pd.concat(map(pd.read_csv, csv_files))
+    groups = results.groupby("Instance")
+    best_lower_bound = groups["Lower bound"].transform("max")
+    best_upper_bound = groups["Upper bound"].transform("min")
+    results["Relative lower bound"] = results["Lower bound"] / best_lower_bound
+    results["Relative upper bound"] = results["Upper bound"] / best_upper_bound
 
     sense = results.loc[0, "Sense"]
     if (sense == "min").any():
@@ -187,7 +193,7 @@ def charts():
     ax2.set_ylim(-0.5, 5.5)
     sns.stripplot(
         x="Solver",
-        y="Gap (%)",
+        y="Gap",
         jitter=0.25,
         data=results[results["Solver"] != "ml-heuristic"],
         ax=ax2,

miplearn/benchmark.py

@@ -94,25 +94,6 @@ class BenchmarkRunner:
         result["Mode"] = solver.mode
         self.results = self.results.append(pd.DataFrame([result]))
 
-        # Compute relative statistics
-        groups = self.results.groupby("Instance")
-        best_lower_bound = groups["Lower bound"].transform("max")
-        best_upper_bound = groups["Upper bound"].transform("min")
-        best_gap = groups["Gap"].transform("min")
-        best_nodes = np.maximum(1, groups["Nodes"].transform("min"))
-        best_wallclock_time = groups["Wallclock time"].transform("min")
-        self.results["Relative lower bound"] = (
-            self.results["Lower bound"] / best_lower_bound
-        )
-        self.results["Relative upper bound"] = (
-            self.results["Upper bound"] / best_upper_bound
-        )
-        self.results["Relative wallclock time"] = (
-            self.results["Wallclock time"] / best_wallclock_time
-        )
-        self.results["Relative Gap"] = self.results["Gap"] / best_gap
-        self.results["Relative Nodes"] = self.results["Nodes"] / best_nodes
-
     def _silence_miplearn_logger(self):
         miplearn_logger = logging.getLogger("miplearn")
         self.prev_log_level = miplearn_logger.getEffectiveLevel()

miplearn/tests/test_benchmark.py

@@ -27,11 +27,11 @@ def test_benchmark():
     benchmark = BenchmarkRunner(test_solvers)
     benchmark.fit(train_instances)
     benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
-    assert benchmark.raw_results().values.shape == (12, 19)
+    assert benchmark.raw_results().values.shape == (12, 14)
 
     benchmark.save_results("/tmp/benchmark.csv")
     assert os.path.isfile("/tmp/benchmark.csv")
 
     benchmark = BenchmarkRunner(test_solvers)
     benchmark.load_results("/tmp/benchmark.csv")
-    assert benchmark.raw_results().values.shape == (12, 19)
+    assert benchmark.raw_results().values.shape == (12, 14)