Move collected data to instance.training_data

miplearn/solvers/learning.py

@@ -115,11 +115,11 @@ class LearningSolver:
 
     def solve(
         self,
-        instance,
-        model=None,
-        output="",
-        tee=False,
-    ):
+        instance: Union[Instance, str],
+        model: Any = None,
+        output: str = "",
+        tee: bool = False,
+    ) -> MIPSolveStats:
         """
         Solves the given instance. If trained machine-learning models are
         available, they will be used to accelerate the solution process.
@@ -127,20 +127,9 @@ class LearningSolver:
         The argument `instance` may be either an Instance object or a
         filename pointing to a pickled Instance object.
 
-        This method modifies the instance object. Specifically, the following
-        properties are set:
-
-            - instance.lp_solution
-            - instance.lp_value
-            - instance.lower_bound
-            - instance.upper_bound
-            - instance.solution
-            - instance.solver_log
-
-        Additional solver components may set additional properties. Please
-        see their documentation for more details. If a filename is provided,
-        then the file is modified in-place. That is, the original file is
-        overwritten.
+        This method adds a new training sample to `instance.training_sample`.
+        If a filename is provided, then the file is modified in-place. That is,
+        the original file is overwritten.
 
         If `solver.solve_lp_first` is False, the properties lp_solution and
         lp_value will be set to dummy values.
@@ -190,7 +179,7 @@ class LearningSolver:
 
     def _solve(
         self,
-        instance: Instance,
+        instance: Union[Instance, str],
         model: Any = None,
         output: str = "",
         tee: bool = False,
@@ -211,14 +200,18 @@ class LearningSolver:
                 fileformat = "pickle"
                 with open(filename, "rb") as file:
                     instance = pickle.load(cast(IO[bytes], file))
+        assert isinstance(instance, Instance)
 
         # Generate model
         if model is None:
             with RedirectOutput([]):
                 model = instance.to_model()
 
-        # Initialize training data
+        # Initialize training sample
         training_sample: TrainingSample = {}
+        if not hasattr(instance, "training_data"):
+            instance.training_data = []
+        instance.training_data += [training_sample]
 
         # Initialize internal solver
         self.tee = tee
@@ -275,11 +268,6 @@ class LearningSolver:
         for component in self.components.values():
             component.after_solve(self, instance, model, stats, training_sample)
 
-        # Append training data
-        if not hasattr(instance, "training_data"):
-            instance.training_data = []
-        instance.training_data += [training_sample]
-
         # Write to file, if necessary
         if filename is not None and output is not None:
             output_filename = output
@@ -350,7 +338,7 @@ class LearningSolver:
         self._restore_miplearn_logger()
         return stats
 
-    def fit(self, training_instances):
+    def fit(self, training_instances: Union[List[str], List[Instance]]) -> None:
         if len(training_instances) == 0:
             return
         for component in self.components.values():