Combine np.ndarray conversion with rounding

tests/solvers/test_learning_solver.py

@@ -16,8 +16,8 @@ from miplearn.solvers.internal import InternalSolver
 from miplearn.solvers.learning import LearningSolver
 
 # noinspection PyUnresolvedReferences
-from miplearn.solvers.tests import _round
 from tests.solvers.test_internal_solver import internal_solvers
+from miplearn.solvers.tests import assert_equals
 
 logger = logging.getLogger(__name__)
 
@@ -51,7 +51,7 @@ def test_learning_solver(
             after_lp = sample.after_lp
             assert after_lp is not None
             assert after_lp.variables is not None
-            assert _round(after_lp.variables.values) == [1.0, 0.923077, 1.0, 0.0, 67.0]
+            assert_equals(after_lp.variables.values, [1.0, 0.923077, 1.0, 0.0, 67.0])
             assert after_lp.lp_solve is not None
             assert after_lp.lp_solve.lp_value is not None
             assert round(after_lp.lp_solve.lp_value, 3) == 1287.923

tests/test_features.py

@@ -2,6 +2,8 @@
 #  Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
 #  Released under the modified BSD license. See COPYING.md for more details.
 
+import numpy as np
+
 from miplearn.features import (
     FeaturesExtractor,
     InstanceFeatures,
@@ -9,11 +11,7 @@ from miplearn.features import (
     ConstraintFeatures,
 )
 from miplearn.solvers.gurobi import GurobiSolver
-from miplearn.solvers.tests import (
-    assert_equals,
-    _round,
-)
-import numpy as np
+from miplearn.solvers.tests import assert_equals
 
 inf = float("inf")
 
@@ -30,7 +28,7 @@ def test_knapsack() -> None:
     assert features.instance is not None
 
     assert_equals(
-        _round(features.variables),
+        features.variables,
         VariableFeatures(
             names=["x[0]", "x[1]", "x[2]", "x[3]", "z"],
             basis_status=["U", "B", "U", "L", "U"],
@@ -64,7 +62,7 @@ def test_knapsack() -> None:
         ),
     )
     assert_equals(
-        _round(features.constraints),
+        features.constraints,
         ConstraintFeatures(
             basis_status=["N"],
             categories=["eq_capacity"],