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Use np.ndarray in Variables

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This commit is contained in:
Alinson S. Xavier
2021-08-05 15:42:19 -05:00
parent b6426462a1
commit 0c4b0ea81a
10 changed files with 110 additions and 66 deletions
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2
tests/components/test_primal.py
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@@ -24,7 +24,7 @@ def sample() -> Sample:
{
"static_var_names": ["x[0]", "x[1]", "x[2]", "x[3]"],
"static_var_categories": ["default", None, "default", "default"],
"mip_var_values": [0.0, 1.0, 1.0, 0.0],
"mip_var_values": np.array([0.0, 1.0, 1.0, 0.0]),
"static_instance_features": [5.0],
"static_var_features": [
[0.0, 0.0],

3
tests/features/test_extractor.py
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@@ -32,7 +32,8 @@ def test_knapsack() -> None:
# -------------------------------------------------------
extractor.extract_after_load_features(instance, solver, sample)
assert_equals(
sample.get_vector("static_var_names"), ["x[0]", "x[1]", "x[2]", "x[3]", "z"]
sample.get_vector("static_var_names"),
["x[0]", "x[1]", "x[2]", "x[3]", "z"],
)
assert_equals(
sample.get_vector("static_var_lower_bounds"), [0.0, 0.0, 0.0, 0.0, 0.0]

2
tests/instance/test_file.py
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@@ -17,7 +17,7 @@ def test_usage() -> None:
# Save instance to disk
filename = tempfile.mktemp()
FileInstance.save(original, filename)
sample = Hdf5Sample(filename)
sample = Hdf5Sample(filename, check_data=True)
assert len(sample.get_bytes("pickled")) > 0
# Solve instance from disk

40
tests/problems/test_tsp.py
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@@ -9,6 +9,7 @@ from scipy.stats import uniform, randint
from miplearn.problems.tsp import TravelingSalesmanGenerator, TravelingSalesmanInstance
from miplearn.solvers.learning import LearningSolver
from miplearn.solvers.tests import assert_equals
def test_generator() -> None:
@@ -41,7 +42,7 @@ def test_instance() -> None:
solver.solve(instance)
assert len(instance.get_samples()) == 1
sample = instance.get_samples()[0]
assert sample.get_vector("mip_var_values") == [1.0, 0.0, 1.0, 1.0, 0.0, 1.0]
assert_equals(sample.get_vector("mip_var_values"), [1.0, 0.0, 1.0, 1.0, 0.0, 1.0])
assert sample.get_scalar("mip_lower_bound") == 4.0
assert sample.get_scalar("mip_upper_bound") == 4.0
@@ -68,22 +69,25 @@ def test_subtour() -> None:
lazy_enforced = sample.get_set("mip_constr_lazy_enforced")
assert lazy_enforced is not None
assert len(lazy_enforced) > 0
assert sample.get_vector("mip_var_values") == [
1.0,
0.0,
0.0,
1.0,
0.0,
1.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
1.0,
1.0,
]
assert_equals(
sample.get_vector("mip_var_values"),
[
1.0,
0.0,
0.0,
1.0,
0.0,
1.0,
0.0,
0.0,
0.0,
1.0,
0.0,
0.0,
0.0,
1.0,
1.0,
],
)
solver.fit([instance])
solver.solve(instance)

4
tests/solvers/test_learning_solver.py
View File

@@ -38,7 +38,9 @@ def test_learning_solver(
assert len(instance.get_samples()) > 0
sample = instance.get_samples()[0]
assert sample.get_vector("mip_var_values") == [1.0, 0.0, 1.0, 1.0, 61.0]
assert_equals(
sample.get_vector("mip_var_values"), [1.0, 0.0, 1.0, 1.0, 61.0]
)
assert sample.get_scalar("mip_lower_bound") == 1183.0
assert sample.get_scalar("mip_upper_bound") == 1183.0
mip_log = sample.get_scalar("mip_log")