Implement tests for ConvertTightIneqsIntoEqsStep

miplearn/components/steps/convert_tight.py

@@ -5,6 +5,7 @@
 import logging
 from copy import deepcopy
 
+import numpy as np
 from tqdm import tqdm
 
 from miplearn import Component
@@ -124,7 +125,7 @@ class ConvertTightIneqsIntoEqsStep(Component):
             if category not in self.classifiers:
                 continue
             y[category] = []
-            # x_cat = np.array(x_cat)
+            x_cat = np.array(x_cat)
             proba = self.classifiers[category].predict_proba(x_cat)
             for i in range(len(proba)):
                 if proba[i][1] >= self.threshold:

miplearn/components/steps/tests/convert_tight_test.py

@@ -0,0 +1,34 @@
+from miplearn import LearningSolver, GurobiSolver
+from miplearn.components.steps.convert_tight import ConvertTightIneqsIntoEqsStep
+from miplearn.components.steps.relax_integrality import RelaxIntegralityStep
+from miplearn.problems.knapsack import GurobiKnapsackInstance
+
+
+def test_convert_tight_usage():
+    instance = GurobiKnapsackInstance(
+        weights=[3.0, 5.0, 10.0],
+        prices=[1.0, 1.0, 1.0],
+        capacity=16.0,
+    )
+    solver = LearningSolver(
+        solver=GurobiSolver(),
+        components=[
+            RelaxIntegralityStep(),
+            ConvertTightIneqsIntoEqsStep(),
+        ],
+    )
+
+    # Solve original problem
+    solver.solve(instance)
+    original_upper_bound = instance.upper_bound
+
+    # Should collect training data
+    assert hasattr(instance, "slacks")
+    assert instance.slacks["eq_capacity"] == 0.0
+
+    # Fit and resolve
+    solver.fit([instance])
+    solver.solve(instance)
+
+    # Objective value should be the same
+    assert instance.upper_bound == original_upper_bound

miplearn/problems/stab.py

@@ -2,14 +2,14 @@
 #  Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+import networkx as nx
 import numpy as np
 import pyomo.environ as pe
-import networkx as nx
+from scipy.stats import uniform, randint
-from miplearn import Instance
-import random
-from scipy.stats import uniform, randint, bernoulli
 from scipy.stats.distributions import rv_frozen
 
+from miplearn import Instance
+
 
 class ChallengeA:
     def __init__(