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Reformat source code with Black; add pre-commit hooks and CI checks

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This commit is contained in:
Alinson S. Xavier
2020-12-05 10:59:33 -06:00
parent 3823931382
commit d99600f101
49 changed files with 1291 additions and 972 deletions
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12
miplearn/tests/__init__.py
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@@ -8,14 +8,14 @@ from miplearn.problems.knapsack import KnapsackInstance
def get_test_pyomo_instances():
instances = [
KnapsackInstance(
weights=[23., 26., 20., 18.],
prices=[505., 352., 458., 220.],
capacity=67.,
weights=[23.0, 26.0, 20.0, 18.0],
prices=[505.0, 352.0, 458.0, 220.0],
capacity=67.0,
),
KnapsackInstance(
weights=[25., 30., 22., 18.],
prices=[500., 365., 420., 150.],
capacity=70.,
weights=[25.0, 30.0, 22.0, 18.0],
prices=[500.0, 365.0, 420.0, 150.0],
capacity=70.0,
),
]
models = [instance.to_model() for instance in instances]

13
miplearn/tests/test_benchmark.py
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@@ -11,8 +11,9 @@ from scipy.stats import randint
def test_benchmark():
# Generate training and test instances
train_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(5)
test_instances = MaxWeightStableSetGenerator(n=randint(low=25, high=26)).generate(3)
generator = MaxWeightStableSetGenerator(n=randint(low=25, high=26))
train_instances = generator.generate(5)
test_instances = generator.generate(3)
# Training phase...
training_solver = LearningSolver()
@@ -26,11 +27,11 @@ def test_benchmark():
benchmark = BenchmarkRunner(test_solvers)
benchmark.fit(train_instances)
benchmark.parallel_solve(test_instances, n_jobs=2, n_trials=2)
assert benchmark.raw_results().values.shape == (12,16)
assert benchmark.raw_results().values.shape == (12, 16)
benchmark.save_results("/tmp/benchmark.csv")
assert os.path.isfile("/tmp/benchmark.csv")
benchmark = BenchmarkRunner(test_solvers)
benchmark.load_results("/tmp/benchmark.csv")
assert benchmark.raw_results().values.shape == (12,16)
assert benchmark.raw_results().values.shape == (12, 16)

58
miplearn/tests/test_extractors.py
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@@ -3,25 +3,28 @@
# Released under the modified BSD license. See COPYING.md for more details.
from miplearn.problems.knapsack import KnapsackInstance
from miplearn import (LearningSolver,
SolutionExtractor,
InstanceFeaturesExtractor,
VariableFeaturesExtractor,
)
from miplearn import (
LearningSolver,
SolutionExtractor,
InstanceFeaturesExtractor,
VariableFeaturesExtractor,
)
import numpy as np
import pyomo.environ as pe
def _get_instances():
instances = [
KnapsackInstance(weights=[1., 2., 3.],
prices=[10., 20., 30.],
capacity=2.5,
),
KnapsackInstance(weights=[3., 4., 5.],
prices=[20., 30., 40.],
capacity=4.5,
),
KnapsackInstance(
weights=[1.0, 2.0, 3.0],
prices=[10.0, 20.0, 30.0],
capacity=2.5,
),
KnapsackInstance(
weights=[3.0, 4.0, 5.0],
prices=[20.0, 30.0, 40.0],
capacity=4.5,
),
]
models = [instance.to_model() for instance in instances]
solver = LearningSolver()
@@ -38,25 +41,30 @@ def test_solution_extractor():
assert isinstance(features["default"], np.ndarray)
assert features["default"].shape == (6, 2)
assert features["default"].ravel().tolist() == [
1., 0.,
0., 1.,
1., 0.,
1., 0.,
0., 1.,
1., 0.,
1.0,
0.0,
0.0,
1.0,
1.0,
0.0,
1.0,
0.0,
0.0,
1.0,
1.0,
0.0,
]
def test_instance_features_extractor():
instances, models = _get_instances()
features = InstanceFeaturesExtractor().extract(instances)
assert features.shape == (2,3)
assert features.shape == (2, 3)
def test_variable_features_extractor():
instances, models = _get_instances()
features = VariableFeaturesExtractor().extract(instances)
assert isinstance(features, dict)
assert "default" in features
assert features["default"].shape == (6,5)
assert features["default"].shape == (6, 5)