Implement load; update fit

miplearn/solvers/learning.py

@@ -5,7 +5,7 @@
 import logging
 import time
 import traceback
-from typing import Optional, List, Any, cast, Dict, Tuple, Callable, IO
+from typing import Optional, List, Any, cast, Dict, Tuple, Callable, IO, Union
 
 from overrides import overrides
 from p_tqdm import p_map
@@ -24,13 +24,18 @@ from miplearn.solvers.pyomo.gurobi import GurobiPyomoSolver
 from miplearn.types import LearningSolveStats
 import gzip
 import pickle
+import miplearn
 from os.path import exists
 
 logger = logging.getLogger(__name__)
 
 
-class InstanceWrapper(Instance):
-    def __init__(self, data_filename: Any, build_model: Callable):
+class FileInstanceWrapper(Instance):
+    def __init__(
+        self,
+        data_filename: Any,
+        build_model: Callable,
+    ):
         super().__init__()
         assert data_filename.endswith(".pkl.gz")
         self.filename = data_filename
@@ -43,9 +48,7 @@ class InstanceWrapper(Instance):
 
     @overrides
     def to_model(self) -> Any:
-        with gzip.GzipFile(self.filename, "rb") as file:
-            data = pickle.load(cast(IO[bytes], file))
-            return self.build_model(data)
+        return miplearn.load(self.filename, self.build_model)
 
     @overrides
     def create_sample(self) -> Sample:
@@ -56,6 +59,17 @@ class InstanceWrapper(Instance):
         return [self.sample]
 
 
+class MemoryInstanceWrapper(Instance):
+    def __init__(self, model):
+        super().__init__()
+        assert model is not None
+        self.model = model
+
+    @overrides
+    def to_model(self) -> Any:
+        return self.model
+
+
 class _GlobalVariables:
     def __init__(self) -> None:
         self.solver: Optional[LearningSolver] = None
@@ -361,18 +375,24 @@ class LearningSolver:
 
     def solve(
         self,
-        filenames: List[str],
-        build_model: Callable,
-        tee: bool = True,
+        arg: Union[Any, List[str]],
+        build_model: Callable = None,
+        tee: bool = False,
     ) -> List[LearningSolveStats]:
-        stats = []
-        for f in filenames:
-            s = self._solve(InstanceWrapper(f, build_model), tee=tee)
-            stats.append(s)
-        return stats
+        if isinstance(arg, list):
+            assert build_model is not None
+            stats = []
+            for i in arg:
+                s = self._solve(FileInstanceWrapper(i, build_model), tee=tee)
+                stats.append(s)
+            return stats
+        else:
+            return self._solve(MemoryInstanceWrapper(arg), tee=tee)
 
     def fit(self, filenames: List[str], build_model: Callable) -> None:
-        instances: List[Instance] = [InstanceWrapper(f, build_model) for f in filenames]
+        instances: List[Instance] = [
+            FileInstanceWrapper(f, build_model) for f in filenames
+        ]
         self._fit(instances)
 
     def parallel_solve(

miplearn/solvers/pyomo/base.py

@@ -322,8 +322,14 @@ class BasePyomoSolver(InternalSolver):
 
                     # Bounds
                     lb, ub = v.bounds
-                    upper_bounds.append(float(ub))
-                    lower_bounds.append(float(lb))
+                    if ub is not None:
+                        upper_bounds.append(float(ub))
+                    else:
+                        upper_bounds.append(float("inf"))
+                    if lb is not None:
+                        lower_bounds.append(float(lb))
+                    else:
+                        lower_bounds.append(-float("inf"))
 
                     # Objective coefficient
                     if v.name in self._obj:
@@ -391,7 +397,9 @@ class BasePyomoSolver(InternalSolver):
     ) -> None:
         if model is None:
             model = instance.to_model()
-        assert isinstance(model, pe.ConcreteModel)
+        assert isinstance(
+            model, pe.ConcreteModel
+        ), f"expected pe.ConcreteModel; found {model.__class__} instead"
         self.instance = instance
         self.model = model
         self.model.extra_constraints = ConstraintList()