Replace Hashable by str

miplearn/components/primal.py

@@ -3,15 +3,7 @@
 #  Released under the modified BSD license. See COPYING.md for more details.
 
 import logging
-from typing import (
-    Dict,
-    List,
-    Hashable,
-    Any,
-    TYPE_CHECKING,
-    Tuple,
-    Optional,
-)
+from typing import Dict, List, Any, TYPE_CHECKING, Tuple, Optional
 
 import numpy as np
 from overrides import overrides
@@ -55,8 +47,8 @@ class PrimalSolutionComponent(Component):
         assert isinstance(threshold, Threshold)
         assert mode in ["exact", "heuristic"]
         self.mode = mode
-        self.classifiers: Dict[Hashable, Classifier] = {}
-        self.thresholds: Dict[Hashable, Threshold] = {}
+        self.classifiers: Dict[str, Classifier] = {}
+        self.thresholds: Dict[str, Threshold] = {}
         self.threshold_prototype = threshold
         self.classifier_prototype = classifier
 
@@ -128,7 +120,7 @@ class PrimalSolutionComponent(Component):
 
         # Convert y_pred into solution
         solution: Solution = {v: None for v in var_names}
-        category_offset: Dict[Hashable, int] = {cat: 0 for cat in x.keys()}
+        category_offset: Dict[str, int] = {cat: 0 for cat in x.keys()}
         for (i, var_name) in enumerate(var_names):
             category = var_categories[i]
             if category not in category_offset:
@@ -194,7 +186,7 @@ class PrimalSolutionComponent(Component):
         self,
         _: Optional[Instance],
         sample: Sample,
-    ) -> Dict[Hashable, Dict[str, float]]:
+    ) -> Dict[str, Dict[str, float]]:
         mip_var_values = sample.get("mip_var_values")
         var_names = sample.get("var_names")
         assert mip_var_values is not None
@@ -221,13 +213,13 @@ class PrimalSolutionComponent(Component):
         pred_one_negative = vars_all - pred_one_positive
         pred_zero_negative = vars_all - pred_zero_positive
         return {
-            0: classifier_evaluation_dict(
+            "0": classifier_evaluation_dict(
                 tp=len(pred_zero_positive & vars_zero),
                 tn=len(pred_zero_negative & vars_one),
                 fp=len(pred_zero_positive & vars_one),
                 fn=len(pred_zero_negative & vars_zero),
             ),
-            1: classifier_evaluation_dict(
+            "1": classifier_evaluation_dict(
                 tp=len(pred_one_positive & vars_one),
                 tn=len(pred_one_negative & vars_zero),
                 fp=len(pred_one_positive & vars_zero),
@@ -238,8 +230,8 @@ class PrimalSolutionComponent(Component):
     @overrides
     def fit_xy(
         self,
-        x: Dict[Hashable, np.ndarray],
-        y: Dict[Hashable, np.ndarray],
+        x: Dict[str, np.ndarray],
+        y: Dict[str, np.ndarray],
     ) -> None:
         for category in x.keys():
             clf = self.classifier_prototype.clone()