LearningSolver: return model

miplearn/solvers/learning.py

@@ -3,7 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 from os.path import exists
 from tempfile import NamedTemporaryFile
-from typing import List, Any, Union, Dict, Callable, Optional
+from typing import List, Any, Union, Dict, Callable, Optional, Tuple
 
 from miplearn.h5 import H5File
 from miplearn.io import _to_h5_filename
@@ -25,7 +25,7 @@ class LearningSolver:
         self,
         model: Union[str, AbstractModel],
         build_model: Optional[Callable] = None,
-    ) -> Dict[str, Any]:
+    ) -> Tuple[AbstractModel, Dict[str, Any]]:
         h5_filename, mode = NamedTemporaryFile().name, "w"
         if isinstance(model, str):
             assert build_model is not None
@@ -51,4 +51,4 @@ class LearningSolver:
             model.optimize()
             model.extract_after_mip(h5)
 
-        return stats
+        return model, stats

tests/components/cuts/test_mem.py

@@ -71,5 +71,5 @@ def test_usage_stab(
         comp = MemorizingCutsComponent(clf=clf, extractor=default_extractor)
         solver = LearningSolver(components=[comp])
         solver.fit(data_filenames)
-        stats = solver.optimize(data_filenames[0], build_model)  # type: ignore
+        model, stats = solver.optimize(data_filenames[0], build_model)  # type: ignore
         assert stats["Cuts: AOT"] > 0

tests/components/lazy/test_mem.py

@@ -65,5 +65,5 @@ def test_usage_tsp(
         comp = MemorizingLazyComponent(clf=clf, extractor=default_extractor)
         solver = LearningSolver(components=[comp])
         solver.fit(data_filenames)
-        stats = solver.optimize(data_filenames[0], build_model)  # type: ignore
+        model, stats = solver.optimize(data_filenames[0], build_model)  # type: ignore
         assert stats["Lazy Constraints: AOT"] > 0