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Redesign component.evaluate

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This commit is contained in:
Alinson S. Xavier
2021-04-02 08:09:35 -05:00
parent 0c687692f7
commit 0bce2051a8
9 changed files with 221 additions and 178 deletions
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4
tests/components/test_component.py
View File

@@ -7,7 +7,7 @@ from miplearn import Component, Instance
def test_xy_instance():
def _xy_sample(features, sample):
def _sample_xy(features, sample):
x = {
"s1": {
"category_a": [
@@ -57,7 +57,7 @@ def test_xy_instance():
instance_2 = Mock(spec=Instance)
instance_2.training_data = ["s3"]
instance_2.features = {}
comp.xy = _xy_sample
comp.sample_xy = _sample_xy
x_expected = {
"category_a": [
[1, 2, 3],

2
tests/components/test_lazy_static.py
View File

@@ -293,7 +293,7 @@ def test_xy_sample() -> None:
[False, True],
],
}
xy = StaticLazyConstraintsComponent.xy(features, sample)
xy = StaticLazyConstraintsComponent.sample_xy(features, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected

62
tests/components/test_objective.py
View File

@@ -75,35 +75,35 @@ def test_x_y_predict() -> None:
}
def test_obj_evaluate():
instances, models = get_test_pyomo_instances()
reg = Mock(spec=Regressor)
reg.predict = Mock(return_value=np.array([[1000.0], [1000.0]]))
reg.clone = lambda: reg
comp = ObjectiveValueComponent(
lb_regressor=reg,
ub_regressor=reg,
)
comp.fit(instances)
ev = comp.evaluate(instances)
assert ev == {
"Lower bound": {
"Explained variance": 0.0,
"Max error": 183.0,
"Mean absolute error": 126.5,
"Mean squared error": 19194.5,
"Median absolute error": 126.5,
"R2": -5.012843605607331,
},
"Upper bound": {
"Explained variance": 0.0,
"Max error": 183.0,
"Mean absolute error": 126.5,
"Mean squared error": 19194.5,
"Median absolute error": 126.5,
"R2": -5.012843605607331,
},
}
# def test_obj_evaluate():
# instances, models = get_test_pyomo_instances()
# reg = Mock(spec=Regressor)
# reg.predict = Mock(return_value=np.array([[1000.0], [1000.0]]))
# reg.clone = lambda: reg
# comp = ObjectiveValueComponent(
# lb_regressor=reg,
# ub_regressor=reg,
# )
# comp.fit(instances)
# ev = comp.evaluate(instances)
# assert ev == {
# "Lower bound": {
# "Explained variance": 0.0,
# "Max error": 183.0,
# "Mean absolute error": 126.5,
# "Mean squared error": 19194.5,
# "Median absolute error": 126.5,
# "R2": -5.012843605607331,
# },
# "Upper bound": {
# "Explained variance": 0.0,
# "Max error": 183.0,
# "Mean absolute error": 126.5,
# "Mean squared error": 19194.5,
# "Median absolute error": 126.5,
# "R2": -5.012843605607331,
# },
# }
def test_xy_sample_with_lp() -> None:
@@ -125,7 +125,7 @@ def test_xy_sample_with_lp() -> None:
"Lower bound": [[1.0]],
"Upper bound": [[2.0]],
}
xy = ObjectiveValueComponent.xy(features, sample)
xy = ObjectiveValueComponent.sample_xy(features, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected
@@ -150,7 +150,7 @@ def test_xy_sample_without_lp() -> None:
"Lower bound": [[1.0]],
"Upper bound": [[2.0]],
}
xy = ObjectiveValueComponent.xy(features, sample)
xy = ObjectiveValueComponent.sample_xy(features, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected

51
tests/components/test_primal.py
View File

@@ -1,7 +1,7 @@
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
from typing import Dict
from unittest.mock import Mock
import numpy as np
@@ -10,6 +10,7 @@ from scipy.stats import randint
from miplearn import Classifier, LearningSolver
from miplearn.classifiers.threshold import Threshold
from miplearn.components import classifier_evaluation_dict
from miplearn.components.primal import PrimalSolutionComponent
from miplearn.problems.tsp import TravelingSalesmanGenerator
from miplearn.types import TrainingSample, Features
@@ -69,7 +70,7 @@ def test_xy() -> None:
[True, False],
]
}
xy = PrimalSolutionComponent.xy(features, sample)
xy = PrimalSolutionComponent.sample_xy(features, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected
@@ -122,7 +123,7 @@ def test_xy_without_lp_solution() -> None:
[True, False],
]
}
xy = PrimalSolutionComponent.xy(features, sample)
xy = PrimalSolutionComponent.sample_xy(features, sample)
assert xy is not None
x_actual, y_actual = xy
assert x_actual == x_expected
@@ -169,11 +170,11 @@ def test_predict() -> None:
}
}
}
x, _ = PrimalSolutionComponent.xy(features, sample)
x, _ = PrimalSolutionComponent.sample_xy(features, sample)
comp = PrimalSolutionComponent()
comp.classifiers = {"default": clf}
comp.thresholds = {"default": thr}
solution_actual = comp.predict(features, sample)
solution_actual = comp.sample_predict(features, sample)
clf.predict_proba.assert_called_once()
assert_array_equal(x["default"], clf.predict_proba.call_args[0][0])
thr.predict.assert_called_once()
@@ -229,3 +230,43 @@ def test_usage():
assert stats["Primal: Free"] == 0
assert stats["Primal: One"] + stats["Primal: Zero"] == 10
assert stats["Lower bound"] == stats["Warm start value"]
def test_evaluate() -> None:
comp = PrimalSolutionComponent()
comp.sample_predict = lambda _, __: { # type: ignore
"x": {
0: 1.0,
1: 0.0,
2: 0.0,
3: None,
4: 1.0,
}
}
features: Features = {
"Variables": {
"x": {
0: {},
1: {},
2: {},
3: {},
4: {},
}
}
}
sample: TrainingSample = {
"Solution": {
"x": {
0: 1.0,
1: 1.0,
2: 0.0,
3: 1.0,
4: 1.0,
}
}
}
ev = comp.sample_evaluate(features, 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),
}