From 9c816badfb91e6d8d75cb520d728b9d03e716bcc Mon Sep 17 00:00:00 2001
From: Alinson S Xavier <axavier@anl.gov>
Date: Fri, 31 Jul 2020 17:43:28 -0500
Subject: [PATCH] Add save chart function to BenchmarkRunner

---
 src/python/miplearn/benchmark.py | 85 ++++++++++++++++++++++++++++++--
 1 file changed, 82 insertions(+), 3 deletions(-)

diff --git a/src/python/miplearn/benchmark.py b/src/python/miplearn/benchmark.py
index 28230fe..2bff445 100644
--- a/src/python/miplearn/benchmark.py
+++ b/src/python/miplearn/benchmark.py
@@ -18,13 +18,11 @@ class BenchmarkRunner:
         self.solvers = solvers
         self.results = None
         
-    def solve(self, instances, fit=True, tee=False):
+    def solve(self, instances, tee=False):
         for (name, solver) in self.solvers.items():
             for i in tqdm(range(len((instances)))):
                 results = solver.solve(deepcopy(instances[i]), tee=tee)
                 self._push_result(results, solver=solver, name=name, instance=i)
-                if fit:
-                    solver.fit()
 
     def parallel_solve(self, instances, n_jobs=1, n_trials=1):
         instances = instances * n_trials
@@ -72,6 +70,9 @@ class BenchmarkRunner:
         lb = result["Lower bound"]
         ub = result["Upper bound"]
         gap = (ub - lb) / lb
+        if "Predicted LB" not in result:
+            result["Predicted LB"] = float("nan")
+            result["Predicted UB"] = float("nan")
         self.results = self.results.append({
             "Solver": name,
             "Instance": instance,
@@ -101,3 +102,81 @@ class BenchmarkRunner:
                 self.results["Gap"] / best_gap
         self.results["Relative Nodes"] = \
                 self.results["Nodes"] / best_nodes
+
+    def save_chart(self, filename):
+        import matplotlib.pyplot as plt
+        import seaborn as sns
+        from numpy import median
+        
+        sns.set_style("whitegrid")
+        sns.set_palette("Blues_r")
+        results = self.raw_results()
+        results["Gap (%)"] = results["Gap"] * 100.0
+
+        sense = results.loc[0, "Sense"]
+        if sense == "min":
+            primal_column = "Relative Upper Bound"
+            obj_column = "Upper Bound"
+            predicted_obj_column = "Predicted UB"
+        else:
+            primal_column = "Relative Lower Bound"
+            obj_column = "Lower Bound"
+            predicted_obj_column = "Predicted LB"
+
+        fig, (ax1, ax2, ax3, ax4) = plt.subplots(nrows=1,
+                                                 ncols=4,
+                                                 figsize=(12,4),
+                                                 gridspec_kw={'width_ratios': [2, 1, 1, 2]})
+        
+        # Figure 1: Solver x Wallclock Time
+        sns.stripplot(x="Solver",
+                      y="Wallclock Time",
+                      data=results,
+                      ax=ax1,
+                      jitter=0.25,
+                      size=4.0,
+                   );
+        sns.barplot(x="Solver",
+                    y="Wallclock Time",
+                    data=results,
+                    ax=ax1,
+                    errwidth=0.,
+                    alpha=0.4,
+                    estimator=median,
+                   );
+        ax1.set(ylabel='Wallclock Time (s)')
+        
+        # Figure 2: Solver x Gap (%)
+        ax2.set_ylim(-0.5, 5.5)
+        sns.stripplot(x="Solver",
+                      y="Gap (%)",
+                      jitter=0.25,
+                      data=results[results["Mode"] != "heuristic"],
+                      ax=ax2,
+                      size=4.0,
+                     );
+        
+        # Figure 3: Solver x Primal Value
+        ax3.set_ylim(0.95,1.05)
+        sns.stripplot(x="Solver",
+                      y=primal_column,
+                      jitter=0.25,
+                      data=results[results["Mode"] == "heuristic"],
+                      ax=ax3,
+                     );
+
+        # Figure 4: Predicted vs Actual Objective Value
+        sns.scatterplot(x=obj_column,
+                        y=predicted_obj_column,
+                        hue="Solver",
+                        data=results[results["Mode"] != "heuristic"],
+                        ax=ax4,
+                       );
+        xlim, ylim = ax4.get_xlim(), ax4.get_ylim()
+        ax4.plot([-1e10, 1e10], [-1e10, 1e10], ls='-', color="#cccccc");
+        ax4.set_xlim(xlim)
+        ax4.set_ylim(ylim)
+        ax4.get_legend().remove()
+
+        fig.tight_layout()
+        plt.savefig(filename, bbox_inches='tight', dpi=150)        
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