BenchmarkRunner: add solve method

miplearn/benchmark.py

@@ -3,7 +3,9 @@
 # Written by Alinson S. Xavier <axavier@anl.gov>
 
 from .solvers import LearningSolver
+from copy import deepcopy
 import pandas as pd
+from tqdm.auto import tqdm
 
 class BenchmarkRunner:
     def __init__(self, solvers):
@@ -13,59 +15,23 @@ class BenchmarkRunner:
         self.solvers = solvers
         self.results = None
         
+    def solve(self, instances, fit=True, 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):
-        if self.results is None:
-            self.results = pd.DataFrame(columns=["Solver",
-                                                 "Instance",
-                                                 "Wallclock Time",
-                                                 "Lower Bound",
-                                                 "Upper Bound",
-                                                 "Gap",
-                                                 "Nodes",
-                                                ])
         instances = instances * n_trials
         for (name, solver) in self.solvers.items():
             results = solver.parallel_solve(instances,
                                             n_jobs=n_jobs,
-                                            label=name,
+                                            label="Solve (%s)" % name,
                                             collect_training_data=False)
             for i in range(len(instances)):
-                wallclock_time = None
+                self._push_result(results[i], solver=solver, name=name, instance=i)
-                for key in ["Time", "Wall time", "Wallclock time"]:
-                    if key not in results[i]["Solver"][0].keys():
-                        continue
-                    if str(results[i]["Solver"][0][key]) == "<undefined>":
-                        continue
-                    wallclock_time = float(results[i]["Solver"][0][key])
-                nodes = results[i]["Solver"][0]["Nodes"]
-                lb = results[i]["Problem"][0]["Lower bound"]
-                ub = results[i]["Problem"][0]["Upper bound"]
-                gap = (ub - lb) / lb
-                self.results = self.results.append({
-                    "Solver": name,
-                    "Instance": i,
-                    "Wallclock Time": wallclock_time,
-                    "Lower Bound": lb,
-                    "Upper Bound": ub,
-                    "Gap": gap,
-                    "Nodes": nodes,
-                }, ignore_index=True)
-                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 = 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 raw_results(self):
         return self.results
@@ -83,3 +49,52 @@ class BenchmarkRunner:
     def fit(self):
         for (name, solver) in self.solvers.items():
             solver.fit()
+            
+    def _push_result(self, result, solver, name, instance):
+        if self.results is None:
+            self.results = pd.DataFrame(columns=["Solver",
+                                                 "Instance",
+                                                 "Wallclock Time",
+                                                 "Lower Bound",
+                                                 "Upper Bound",
+                                                 "Gap",
+                                                 "Nodes",
+                                                 "Mode",
+                                                ])
+        wallclock_time = None
+        for key in ["Time", "Wall time", "Wallclock time"]:
+            if key not in result["Solver"][0].keys():
+                continue
+            if str(result["Solver"][0][key]) == "<undefined>":
+                continue
+            wallclock_time = float(result["Solver"][0][key])
+        nodes = result["Solver"][0]["Nodes"]
+        lb = result["Problem"][0]["Lower bound"]
+        ub = result["Problem"][0]["Upper bound"]
+        gap = (ub - lb) / lb
+        self.results = self.results.append({
+            "Solver": name,
+            "Instance": instance,
+            "Wallclock Time": wallclock_time,
+            "Lower Bound": lb,
+            "Upper Bound": ub,
+            "Gap": gap,
+            "Nodes": nodes,
+            "Mode": solver.mode,
+        }, ignore_index=True)
+        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 = 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