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Use np.ndarray for constraint methods in Instance

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Alinson S. Xavier
2021-08-09 20:11:37 -05:00
parent 895cb962b6
commit e852d5cdca
16 changed files with 532 additions and 429 deletions
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71
tests/components/test_static_lazy.py
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@@ -17,6 +17,7 @@ from miplearn.solvers.internal import InternalSolver, Constraints
from miplearn.solvers.learning import LearningSolver
from miplearn.types import (
LearningSolveStats,
ConstraintCategory,
)
@@ -25,11 +26,11 @@ def sample() -> Sample:
sample = MemorySample(
{
"static_constr_categories": [
"type-a",
"type-a",
"type-a",
"type-b",
"type-b",
b"type-a",
b"type-a",
b"type-a",
b"type-b",
b"type-b",
],
"static_constr_lazy": np.array([True, True, True, True, False]),
"static_constr_names": np.array(["c1", "c2", "c3", "c4", "c5"], dtype="S"),
@@ -68,13 +69,13 @@ def test_usage_with_solver(instance: Instance) -> None:
)
component = StaticLazyConstraintsComponent(violation_tolerance=1.0)
component.thresholds["type-a"] = MinProbabilityThreshold([0.5, 0.5])
component.thresholds["type-b"] = MinProbabilityThreshold([0.5, 0.5])
component.thresholds[b"type-a"] = MinProbabilityThreshold([0.5, 0.5])
component.thresholds[b"type-b"] = MinProbabilityThreshold([0.5, 0.5])
component.classifiers = {
"type-a": Mock(spec=Classifier),
"type-b": Mock(spec=Classifier),
b"type-a": Mock(spec=Classifier),
b"type-b": Mock(spec=Classifier),
}
component.classifiers["type-a"].predict_proba = Mock( # type: ignore
component.classifiers[b"type-a"].predict_proba = Mock( # type: ignore
return_value=np.array(
[
[0.00, 1.00], # c1
@@ -83,7 +84,7 @@ def test_usage_with_solver(instance: Instance) -> None:
]
)
)
component.classifiers["type-b"].predict_proba = Mock( # type: ignore
component.classifiers[b"type-b"].predict_proba = Mock( # type: ignore
return_value=np.array(
[
[0.02, 0.98], # c4
@@ -105,8 +106,8 @@ def test_usage_with_solver(instance: Instance) -> None:
)
# Should ask ML to predict whether each lazy constraint should be enforced
component.classifiers["type-a"].predict_proba.assert_called_once()
component.classifiers["type-b"].predict_proba.assert_called_once()
component.classifiers[b"type-a"].predict_proba.assert_called_once()
component.classifiers[b"type-b"].predict_proba.assert_called_once()
# Should ask internal solver to remove some constraints
assert internal.remove_constraints.call_count == 1
@@ -153,18 +154,18 @@ def test_usage_with_solver(instance: Instance) -> None:
def test_sample_predict(sample: Sample) -> None:
comp = StaticLazyConstraintsComponent()
comp.thresholds["type-a"] = MinProbabilityThreshold([0.5, 0.5])
comp.thresholds["type-b"] = MinProbabilityThreshold([0.5, 0.5])
comp.classifiers["type-a"] = Mock(spec=Classifier)
comp.classifiers["type-a"].predict_proba = lambda _: np.array( # type:ignore
comp.thresholds[b"type-a"] = MinProbabilityThreshold([0.5, 0.5])
comp.thresholds[b"type-b"] = MinProbabilityThreshold([0.5, 0.5])
comp.classifiers[b"type-a"] = Mock(spec=Classifier)
comp.classifiers[b"type-a"].predict_proba = lambda _: np.array( # type:ignore
[
[0.0, 1.0], # c1
[0.0, 0.9], # c2
[0.9, 0.1], # c3
]
)
comp.classifiers["type-b"] = Mock(spec=Classifier)
comp.classifiers["type-b"].predict_proba = lambda _: np.array( # type:ignore
comp.classifiers[b"type-b"] = Mock(spec=Classifier)
comp.classifiers[b"type-b"].predict_proba = lambda _: np.array( # type:ignore
[
[0.0, 1.0], # c4
]
@@ -175,17 +176,17 @@ def test_sample_predict(sample: Sample) -> None:
def test_fit_xy() -> None:
x = cast(
Dict[str, np.ndarray],
Dict[ConstraintCategory, np.ndarray],
{
"type-a": np.array([[1.0, 1.0], [1.0, 2.0], [1.0, 3.0]]),
"type-b": np.array([[1.0, 4.0, 0.0]]),
b"type-a": np.array([[1.0, 1.0], [1.0, 2.0], [1.0, 3.0]]),
b"type-b": np.array([[1.0, 4.0, 0.0]]),
},
)
y = cast(
Dict[str, np.ndarray],
Dict[ConstraintCategory, np.ndarray],
{
"type-a": np.array([[False, True], [False, True], [True, False]]),
"type-b": np.array([[False, True]]),
b"type-a": np.array([[False, True], [False, True], [True, False]]),
b"type-b": np.array([[False, True]]),
},
)
clf: Classifier = Mock(spec=Classifier)
@@ -198,15 +199,15 @@ def test_fit_xy() -> None:
)
comp.fit_xy(x, y)
assert clf.clone.call_count == 2
clf_a = comp.classifiers["type-a"]
clf_b = comp.classifiers["type-b"]
clf_a = comp.classifiers[b"type-a"]
clf_b = comp.classifiers[b"type-b"]
assert clf_a.fit.call_count == 1 # type: ignore
assert clf_b.fit.call_count == 1 # type: ignore
assert_array_equal(clf_a.fit.call_args[0][0], x["type-a"]) # type: ignore
assert_array_equal(clf_b.fit.call_args[0][0], x["type-b"]) # type: ignore
assert_array_equal(clf_a.fit.call_args[0][0], x[b"type-a"]) # type: ignore
assert_array_equal(clf_b.fit.call_args[0][0], x[b"type-b"]) # type: ignore
assert thr.clone.call_count == 2
thr_a = comp.thresholds["type-a"]
thr_b = comp.thresholds["type-b"]
thr_a = comp.thresholds[b"type-a"]
thr_b = comp.thresholds[b"type-b"]
assert thr_a.fit.call_count == 1 # type: ignore
assert thr_b.fit.call_count == 1 # type: ignore
assert thr_a.fit.call_args[0][0] == clf_a # type: ignore
@@ -215,12 +216,12 @@ def test_fit_xy() -> None:
def test_sample_xy(sample: Sample) -> None:
x_expected = {
"type-a": [[5.0, 1.0, 1.0], [5.0, 1.0, 2.0], [5.0, 1.0, 3.0]],
"type-b": [[5.0, 1.0, 4.0, 0.0]],
b"type-a": [[5.0, 1.0, 1.0], [5.0, 1.0, 2.0], [5.0, 1.0, 3.0]],
b"type-b": [[5.0, 1.0, 4.0, 0.0]],
}
y_expected = {
"type-a": [[False, True], [False, True], [True, False]],
"type-b": [[False, True]],
b"type-a": [[False, True], [False, True], [True, False]],
b"type-b": [[False, True]],
}
xy = StaticLazyConstraintsComponent().sample_xy(None, sample)
assert xy is not None