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Update PrimalSolutionComponent

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This commit is contained in:
Alinson S. Xavier
2021-04-13 07:23:07 -05:00
parent d7aa31f3eb
commit a9dcdb8e4e
3 changed files with 67 additions and 256 deletions
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232
tests/components/test_primal.py
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@@ -1,7 +1,6 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import cast
from unittest.mock import Mock
import numpy as np
@@ -14,15 +13,14 @@ from miplearn.classifiers.threshold import Threshold
from miplearn.components import classifier_evaluation_dict
from miplearn.components.primal import PrimalSolutionComponent
from miplearn.features import (
TrainingSample,
Variable,
Features,
Sample,
InstanceFeatures,
)
from miplearn.instance.base import Instance
from miplearn.problems.tsp import TravelingSalesmanGenerator
from miplearn.solvers.learning import LearningSolver
from miplearn.solvers.tests import assert_equals
@pytest.fixture
@@ -48,7 +46,7 @@ def sample() -> Sample:
after_mip=Features(
variables={
"x[0]": Variable(value=0.0),
"x[1]": Variable(value=0.0),
"x[1]": Variable(value=1.0),
"x[2]": Variable(value=1.0),
"x[3]": Variable(value=0.0),
}
@@ -89,168 +87,6 @@ def test_xy(sample: Sample) -> None:
assert y_actual == y_expected
def test_xy_old() -> None:
features = Features(
variables={
"x[0]": Variable(
category="default",
user_features=[0.0, 0.0],
),
"x[1]": Variable(
category=None,
),
"x[2]": Variable(
category="default",
user_features=[1.0, 0.0],
),
"x[3]": Variable(
category="default",
user_features=[1.0, 1.0],
),
}
)
instance = Mock(spec=Instance)
instance.features = features
sample = TrainingSample(
solution={
"x[0]": 0.0,
"x[1]": 1.0,
"x[2]": 1.0,
"x[3]": 0.0,
},
lp_solution={
"x[0]": 0.1,
"x[1]": 0.1,
"x[2]": 0.1,
"x[3]": 0.1,
},
)
x_expected = {
"default": [
[0.0, 0.0, 0.1],
[1.0, 0.0, 0.1],
[1.0, 1.0, 0.1],
]
}
y_expected = {
"default": [
[True, False],
[False, True],
[True, False],
]
}
xy = PrimalSolutionComponent().sample_xy_old(instance, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected
assert y_actual == y_expected
def test_xy_without_lp_solution_old() -> None:
features = Features(
variables={
"x[0]": Variable(
category="default",
user_features=[0.0, 0.0],
),
"x[1]": Variable(
category=None,
),
"x[2]": Variable(
category="default",
user_features=[1.0, 0.0],
),
"x[3]": Variable(
category="default",
user_features=[1.0, 1.0],
),
}
)
instance = Mock(spec=Instance)
instance.features = features
sample = TrainingSample(
solution={
"x[0]": 0.0,
"x[1]": 1.0,
"x[2]": 1.0,
"x[3]": 0.0,
},
)
x_expected = {
"default": [
[0.0, 0.0],
[1.0, 0.0],
[1.0, 1.0],
]
}
y_expected = {
"default": [
[True, False],
[False, True],
[True, False],
]
}
xy = PrimalSolutionComponent().sample_xy_old(instance, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected
assert y_actual == y_expected
def test_predict_old() -> None:
clf = Mock(spec=Classifier)
clf.predict_proba = Mock(
return_value=np.array(
[
[0.9, 0.1],
[0.5, 0.5],
[0.1, 0.9],
]
)
)
thr = Mock(spec=Threshold)
thr.predict = Mock(return_value=[0.75, 0.75])
features = Features(
variables={
"x[0]": Variable(
category="default",
user_features=[0.0, 0.0],
),
"x[1]": Variable(
category="default",
user_features=[0.0, 2.0],
),
"x[2]": Variable(
category="default",
user_features=[2.0, 0.0],
),
}
)
instance = Mock(spec=Instance)
instance.features = features
sample = TrainingSample(
lp_solution={
"x[0]": 0.1,
"x[1]": 0.5,
"x[2]": 0.9,
}
)
x, _ = PrimalSolutionComponent().sample_xy_old(instance, sample)
comp = PrimalSolutionComponent()
comp.classifiers = {"default": clf}
comp.thresholds = {"default": thr}
pred = comp.sample_predict(instance, sample)
clf.predict_proba.assert_called_once()
assert_array_equal(x["default"], clf.predict_proba.call_args[0][0])
thr.predict.assert_called_once()
assert_array_equal(x["default"], thr.predict.call_args[0][0])
assert pred == {
"x[0]": 0.0,
"x[1]": None,
"x[2]": 1.0,
}
def test_fit_xy() -> None:
clf = Mock(spec=Classifier)
clf.clone = lambda: Mock(spec=Classifier) # type: ignore
@@ -295,37 +131,49 @@ def test_usage() -> None:
assert stats["mip_lower_bound"] == stats["mip_warm_start_value"]
def test_evaluate_old() -> None:
def test_evaluate(sample: Sample) -> None:
comp = PrimalSolutionComponent()
comp.sample_predict = lambda _, __: { # type: ignore
comp.sample_predict = lambda _: { # type: ignore
"x[0]": 1.0,
"x[1]": 0.0,
"x[1]": 1.0,
"x[2]": 0.0,
"x[3]": None,
"x[4]": 1.0,
}
features: Features = Features(
variables={
"x[0]": Variable(),
"x[1]": Variable(),
"x[2]": Variable(),
"x[3]": Variable(),
"x[4]": Variable(),
}
ev = comp.sample_evaluate(None, sample)
assert_equals(
ev,
{
0: classifier_evaluation_dict(tp=0, fp=1, tn=1, fn=2),
1: classifier_evaluation_dict(tp=1, fp=1, tn=1, fn=1),
},
)
instance = Mock(spec=Instance)
instance.features = features
sample: TrainingSample = TrainingSample(
solution={
"x[0]": 1.0,
"x[1]": 1.0,
"x[2]": 0.0,
"x[3]": 1.0,
"x[4]": 1.0,
}
def test_predict(sample: Sample) -> None:
clf = Mock(spec=Classifier)
clf.predict_proba = Mock(
return_value=np.array(
[
[0.9, 0.1],
[0.5, 0.5],
[0.1, 0.9],
]
)
)
ev = comp.sample_evaluate_old(instance, sample)
assert ev == {
0: classifier_evaluation_dict(tp=1, fp=1, tn=3, fn=0),
1: classifier_evaluation_dict(tp=2, fp=0, tn=1, fn=2),
thr = Mock(spec=Threshold)
thr.predict = Mock(return_value=[0.75, 0.75])
comp = PrimalSolutionComponent()
x, _ = comp.sample_xy(None, sample)
comp.classifiers = {"default": clf}
comp.thresholds = {"default": thr}
pred = comp.sample_predict(sample)
clf.predict_proba.assert_called_once()
thr.predict.assert_called_once()
assert_array_equal(x["default"], clf.predict_proba.call_args[0][0])
assert_array_equal(x["default"], thr.predict.call_args[0][0])
assert pred == {
"x[0]": 0.0,
"x[1]": None,
"x[2]": None,
"x[3]": 1.0,
}