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Convert TrainingSample to dataclass

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This commit is contained in:
Alinson S. Xavier
2021-04-05 20:36:04 -05:00
parent aeed338837
commit b11779817a
15 changed files with 122 additions and 129 deletions
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2
tests/components/steps/test_convert_tight.py
View File

@@ -28,7 +28,7 @@ def test_convert_tight_usage():
original_upper_bound = stats["Upper bound"]
# Should collect training data
assert instance.training_data[0]["slacks"]["eq_capacity"] == 0.0
assert instance.training_data[0].slacks["eq_capacity"] == 0.0
# Fit and resolve
solver.fit([instance])

39
tests/components/steps/test_drop_redundant.py
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@@ -12,6 +12,7 @@ from miplearn.components.steps.drop_redundant import DropRedundantInequalitiesSt
from miplearn.instance import Instance
from miplearn.solvers.internal import InternalSolver
from miplearn.solvers.learning import LearningSolver
from miplearn.types import TrainingSample, Features
from tests.fixtures.infeasible import get_infeasible_instance
from tests.fixtures.redundant import get_instance_with_redundancy
@@ -85,8 +86,8 @@ def test_drop_redundant():
instance=instance,
model=None,
stats={},
features=None,
training_data=None,
features=Features(),
training_data=TrainingSample(),
)
# Should query list of constraints
@@ -129,13 +130,13 @@ def test_drop_redundant():
)
# LearningSolver calls after_solve
training_data = {}
training_data = TrainingSample()
component.after_solve_mip(
solver=solver,
instance=instance,
model=None,
stats={},
features=None,
features=Features(),
training_data=training_data,
)
@@ -143,7 +144,7 @@ def test_drop_redundant():
internal.get_inequality_slacks.assert_called_once()
# Should store constraint slacks in instance object
assert training_data["slacks"] == {
assert training_data.slacks == {
"c1": 0.5,
"c2": 0.0,
"c3": 0.0,
@@ -166,8 +167,8 @@ def test_drop_redundant_with_check_feasibility():
instance=instance,
model=None,
stats={},
features=None,
training_data=None,
features=Features(),
training_data=TrainingSample(),
)
# Assert constraints are extracted
@@ -224,14 +225,14 @@ def test_x_y_fit_predict_evaluate():
# First mock instance
instances[0].training_data = [
{
"slacks": {
TrainingSample(
slacks={
"c1": 0.00,
"c2": 0.05,
"c3": 0.00,
"c4": 30.0,
}
}
)
]
instances[0].get_constraint_category = Mock(
side_effect=lambda cid: {
@@ -251,14 +252,14 @@ def test_x_y_fit_predict_evaluate():
# Second mock instance
instances[1].training_data = [
{
"slacks": {
TrainingSample(
slacks={
"c1": 0.00,
"c3": 0.30,
"c4": 0.00,
"c5": 0.00,
}
}
)
]
instances[1].get_constraint_category = Mock(
side_effect=lambda cid: {
@@ -343,22 +344,22 @@ def test_x_y_fit_predict_evaluate():
def test_x_multiple_solves():
instance = Mock(spec=Instance)
instance.training_data = [
{
"slacks": {
TrainingSample(
slacks={
"c1": 0.00,
"c2": 0.05,
"c3": 0.00,
"c4": 30.0,
}
},
{
"slacks": {
),
TrainingSample(
slacks={
"c1": 0.00,
"c2": 0.00,
"c3": 1.00,
"c4": 0.0,
}
},
),
]
instance.get_constraint_category = Mock(
side_effect=lambda cid: {

10
tests/components/test_lazy_static.py
View File

@@ -23,9 +23,9 @@ from miplearn.types import (
@pytest.fixture
def sample() -> TrainingSample:
return {
"LazyStatic: Enforced": {"c1", "c2", "c4"},
}
return TrainingSample(
lazy_enforced={"c1", "c2", "c4"},
)
@pytest.fixture
@@ -101,7 +101,7 @@ def test_usage_with_solver(features: Features) -> None:
)
)
sample: TrainingSample = {}
sample: TrainingSample = TrainingSample()
stats: LearningSolveStats = {}
# LearningSolver calls before_solve_mip
@@ -152,7 +152,7 @@ def test_usage_with_solver(features: Features) -> None:
)
# Should update training sample
assert sample["LazyStatic: Enforced"] == {"c1", "c2", "c3", "c4"}
assert sample.lazy_enforced == {"c1", "c2", "c3", "c4"}
# Should update stats
assert stats["LazyStatic: Removed"] == 1

26
tests/components/test_objective.py
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@@ -26,27 +26,27 @@ def features() -> Features:
@pytest.fixture
def sample() -> TrainingSample:
return {
"Lower bound": 1.0,
"Upper bound": 2.0,
"LP value": 3.0,
}
return TrainingSample(
lower_bound=1.0,
upper_bound=2.0,
lp_value=3.0,
)
@pytest.fixture
def sample_without_lp() -> TrainingSample:
return {
"Lower bound": 1.0,
"Upper bound": 2.0,
}
return TrainingSample(
lower_bound=1.0,
upper_bound=2.0,
)
@pytest.fixture
def sample_without_ub() -> TrainingSample:
return {
"Lower bound": 1.0,
"LP value": 3.0,
}
return TrainingSample(
lower_bound=1.0,
lp_value=3.0,
)
def test_sample_xy(

26
tests/components/test_primal.py
View File

@@ -38,8 +38,8 @@ def test_xy() -> None:
}
}
)
sample: TrainingSample = {
"Solution": {
sample = TrainingSample(
solution={
"x": {
0: 0.0,
1: 1.0,
@@ -47,7 +47,7 @@ def test_xy() -> None:
3: 0.0,
}
},
"LP solution": {
lp_solution={
"x": {
0: 0.1,
1: 0.1,
@@ -55,7 +55,7 @@ def test_xy() -> None:
3: 0.1,
}
},
}
)
x_expected = {
"default": [
[0.0, 0.0, 0.1],
@@ -99,8 +99,8 @@ def test_xy_without_lp_solution() -> None:
}
}
)
sample: TrainingSample = {
"Solution": {
sample = TrainingSample(
solution={
"x": {
0: 0.0,
1: 1.0,
@@ -108,7 +108,7 @@ def test_xy_without_lp_solution() -> None:
3: 0.0,
}
},
}
)
x_expected = {
"default": [
[0.0, 0.0],
@@ -161,15 +161,15 @@ def test_predict() -> None:
}
}
)
sample: TrainingSample = {
"LP solution": {
sample = TrainingSample(
lp_solution={
"x": {
0: 0.1,
1: 0.5,
2: 0.9,
}
}
}
)
x, _ = PrimalSolutionComponent.sample_xy(features, sample)
comp = PrimalSolutionComponent()
comp.classifiers = {"default": clf}
@@ -254,8 +254,8 @@ def test_evaluate() -> None:
}
}
)
sample: TrainingSample = {
"Solution": {
sample = TrainingSample(
solution={
"x": {
0: 1.0,
1: 1.0,
@@ -264,7 +264,7 @@ def test_evaluate() -> None:
4: 1.0,
}
}
}
)
ev = comp.sample_evaluate(features, sample)
assert ev == {
0: classifier_evaluation_dict(tp=1, fp=1, tn=3, fn=0),

4
tests/problems/test_tsp.py
View File

@@ -39,7 +39,7 @@ def test_instance():
instance = TravelingSalesmanInstance(n_cities, distances)
solver = LearningSolver()
stats = solver.solve(instance)
x = instance.training_data[0]["Solution"]["x"]
x = instance.training_data[0].solution["x"]
assert x[0, 1] == 1.0
assert x[0, 2] == 0.0
assert x[0, 3] == 1.0
@@ -68,7 +68,7 @@ def test_subtour():
solver.solve(instance)
assert hasattr(instance, "found_violated_lazy_constraints")
assert hasattr(instance, "found_violated_user_cuts")
x = instance.training_data[0]["Solution"]["x"]
x = instance.training_data[0].solution["x"]
assert x[0, 1] == 1.0
assert x[0, 4] == 1.0
assert x[1, 2] == 1.0

29
tests/solvers/test_learning_solver.py
View File

@@ -27,22 +27,21 @@ def test_learning_solver():
)
solver.solve(instance)
assert hasattr(instance, "features")
data = instance.training_data[0]
assert data["Solution"]["x"][0] == 1.0
assert data["Solution"]["x"][1] == 0.0
assert data["Solution"]["x"][2] == 1.0
assert data["Solution"]["x"][3] == 1.0
assert data["Lower bound"] == 1183.0
assert data["Upper bound"] == 1183.0
assert round(data["LP solution"]["x"][0], 3) == 1.000
assert round(data["LP solution"]["x"][1], 3) == 0.923
assert round(data["LP solution"]["x"][2], 3) == 1.000
assert round(data["LP solution"]["x"][3], 3) == 0.000
assert round(data["LP value"], 3) == 1287.923
assert len(data["MIP log"]) > 100
sample = instance.training_data[0]
assert sample.solution["x"][0] == 1.0
assert sample.solution["x"][1] == 0.0
assert sample.solution["x"][2] == 1.0
assert sample.solution["x"][3] == 1.0
assert sample.lower_bound == 1183.0
assert sample.upper_bound == 1183.0
assert round(sample.lp_solution["x"][0], 3) == 1.000
assert round(sample.lp_solution["x"][1], 3) == 0.923
assert round(sample.lp_solution["x"][2], 3) == 1.000
assert round(sample.lp_solution["x"][3], 3) == 0.000
assert round(sample.lp_value, 3) == 1287.923
assert len(sample.mip_log) > 100
solver.fit([instance])
solver.solve(instance)
@@ -73,7 +72,7 @@ def test_parallel_solve():
assert len(results) == 10
for instance in instances:
data = instance.training_data[0]
assert len(data["Solution"]["x"].keys()) == 4
assert len(data.solution["x"].keys()) == 4
def test_solve_fit_from_disk():