Replace InstanceIterator by PickleGzInstance

miplearn/components/component.py

@@ -2,10 +2,10 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
-import numpy as np
-from typing import Any, List, Union, TYPE_CHECKING, Tuple, Dict, Optional, Hashable
+from typing import Any, List, TYPE_CHECKING, Tuple, Dict, Hashable
+
+import numpy as np
 
-from miplearn.extractors import InstanceIterator
 from miplearn.instance import Instance
 from miplearn.types import LearningSolveStats, TrainingSample, Features
 
@@ -120,11 +120,11 @@ class Component:
 
     def xy_instances(
         self,
-        instances: Union[List[str], List[Instance]],
+        instances: List[Instance],
     ) -> Tuple[Dict, Dict]:
         x_combined: Dict = {}
         y_combined: Dict = {}
-        for instance in InstanceIterator(instances):
+        for instance in instances:
             assert isinstance(instance, Instance)
             for sample in instance.training_data:
                 xy = self.sample_xy(instance.features, sample)
@@ -141,7 +141,7 @@ class Component:
 
     def fit(
         self,
-        training_instances: Union[List[str], List[Instance]],
+        training_instances: List[Instance],
     ) -> None:
         x, y = self.xy_instances(training_instances)
         for cat in x.keys():
@@ -198,9 +198,9 @@ class Component:
     ) -> None:
         return
 
-    def evaluate(self, instances: Union[List[str], List[Instance]]) -> List:
+    def evaluate(self, instances: List[Instance]) -> List:
         ev = []
-        for instance in InstanceIterator(instances):
+        for instance in instances:
             for sample in instance.training_data:
                 ev += [self.sample_evaluate(instance.features, sample)]
         return ev