DropRedundant: Clear pool before each solve

miplearn/components/steps/drop_redundant.py

@@ -60,6 +60,7 @@ class DropRedundantInequalitiesStep(Component):
         )
         y = self.predict(x)
 
+        self.pool = []
         self.total_dropped = 0
         self.total_restored = 0
         self.total_kept = 0
@@ -102,7 +103,7 @@ class DropRedundantInequalitiesStep(Component):
         x = self.x(training_instances)
         y = self.y(training_instances)
         logger.debug("Fitting...")
-        for category in tqdm(x.keys(), desc="Fit (rlx:drop_ineq)"):
+        for category in tqdm(x.keys(), desc="Fit (drop)"):
             if category not in self.classifiers:
                 self.classifiers[category] = deepcopy(self.classifier_prototype)
             self.classifiers[category].fit(x[category], np.array(y[category]))
@@ -130,7 +131,7 @@ class DropRedundantInequalitiesStep(Component):
         x = {}
         for instance in tqdm(
             InstanceIterator(instances),
-            desc="Extract (rlx:drop_ineq:x)",
+            desc="Extract (drop:x)",
             disable=len(instances) < 5,
         ):
             for training_data in instance.training_data:
@@ -153,7 +154,7 @@ class DropRedundantInequalitiesStep(Component):
         y = {}
         for instance in tqdm(
             InstanceIterator(instances),
-            desc="Extract (rlx:drop_ineq:y)",
+            desc="Extract (drop:y)",
             disable=len(instances) < 5,
         ):
             for training_data in instance.training_data:
@@ -189,9 +190,13 @@ class DropRedundantInequalitiesStep(Component):
         y_true = self.y([instance])
         y_pred = self.predict(x)
         tp, tn, fp, fn = 0, 0, 0, 0
-        for category in y_true.keys():
+        for category in tqdm(
+            y_true.keys(),
+            disable=len(y_true) < 100,
+            desc="Eval (drop)",
+        ):
             for i in range(len(y_true[category])):
-                if y_pred[category][i][1] == 1:
+                if (category in y_pred) and (y_pred[category][i][1] == 1):
                     if y_true[category][i][1] == 1:
                         tp += 1
                     else: