Add after_solve_lp callback; make dict keys consistent

tests/components/steps/test_convert_tight.py

@@ -68,7 +68,7 @@ def test_convert_tight_infeasibility():
     solver = LearningSolver(
         solver=GurobiSolver,
         components=[comp],
-        solve_lp_first=False,
+        solve_lp=False,
     )
     instance = SampleInstance()
     stats = solver.solve(instance)
@@ -91,7 +91,7 @@ def test_convert_tight_suboptimality():
     solver = LearningSolver(
         solver=GurobiSolver,
         components=[comp],
-        solve_lp_first=False,
+        solve_lp=False,
     )
     instance = SampleInstance()
     stats = solver.solve(instance)
@@ -114,7 +114,7 @@ def test_convert_tight_optimal():
     solver = LearningSolver(
         solver=GurobiSolver,
         components=[comp],
-        solve_lp_first=False,
+        solve_lp=False,
     )
     instance = SampleInstance()
     stats = solver.solve(instance)

tests/solvers/test_internal_solver.py

@@ -93,8 +93,8 @@ def test_internal_solver():
 
         stats = solver.solve_lp()
         assert not solver.is_infeasible()
-        assert round(stats["Optimal value"], 3) == 1287.923
-        assert len(stats["Log"]) > 100
+        assert round(stats["LP value"], 3) == 1287.923
+        assert len(stats["LP log"]) > 100
 
         solution = solver.get_solution()
         assert round(solution["x"][0], 3) == 1.000
@@ -104,7 +104,7 @@ def test_internal_solver():
 
         stats = solver.solve(tee=True)
         assert not solver.is_infeasible()
-        assert len(stats["Log"]) > 100
+        assert len(stats["MIP log"]) > 100
         assert stats["Lower bound"] == 1183.0
         assert stats["Upper bound"] == 1183.0
         assert stats["Sense"] == "max"
@@ -198,7 +198,7 @@ def test_infeasible_instance():
 
         stats = solver.solve_lp()
         assert solver.get_solution() is None
-        assert stats["Optimal value"] is None
+        assert stats["LP value"] is None
         assert solver.get_value("x", 0) is None
 
 

tests/solvers/test_learning_solver.py

@@ -57,7 +57,7 @@ def test_solve_without_lp():
         instance = _get_knapsack_instance(internal_solver)
         solver = LearningSolver(
             solver=internal_solver,
-            solve_lp_first=False,
+            solve_lp=False,
         )
         solver.solve(instance)
         solver.fit([instance])

tests/test_benchmark.py

@@ -29,7 +29,7 @@ def test_benchmark():
     benchmark = BenchmarkRunner(test_solvers)
     benchmark.fit(train_instances)
     benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
-    assert benchmark.results.values.shape == (12, 17)
+    assert benchmark.results.values.shape == (12, 18)
 
     benchmark.write_csv("/tmp/benchmark.csv")
     assert os.path.isfile("/tmp/benchmark.csv")