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Implement PrimalSolutionComponent; remove deprecated predictors

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This commit is contained in:
Alinson S. Xavier
2020-02-24 10:01:21 -06:00
parent ccd694af9b
commit a2fbb9f8d8
12 changed files with 140 additions and 669 deletions
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41
miplearn/components/tests/test_warmstart.py
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# 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 miplearn import WarmStartComponent, LearningSolver
from miplearn.problems.knapsack import KnapsackInstance
import numpy as np
import tempfile
def _get_instances():
return [
KnapsackInstance(
weights=[23., 26., 20., 18.],
prices=[505., 352., 458., 220.],
capacity=67.,
),
] * 2
# def test_warm_start_save_load():
# state_file = tempfile.NamedTemporaryFile(mode="r")
# solver = LearningSolver(components={"warm-start": WarmStartComponent()})
# solver.parallel_solve(_get_instances(), n_jobs=2)
# solver.fit()
# comp = solver.components["warm-start"]
# assert comp.x_train["default"].shape == (8, 6)
# assert comp.y_train["default"].shape == (8, 2)
# assert ("default", 0) in comp.predictors.keys()
# assert ("default", 1) in comp.predictors.keys()
# solver.save_state(state_file.name)
# solver.solve(_get_instances()[0])
# solver = LearningSolver(components={"warm-start": WarmStartComponent()})
# solver.load_state(state_file.name)
# comp = solver.components["warm-start"]
# assert comp.x_train["default"].shape == (8, 6)
# assert comp.y_train["default"].shape == (8, 2)
# assert ("default", 0) in comp.predictors.keys()
# assert ("default", 1) in comp.predictors.keys()

88
miplearn/components/tests/test_warmstart_knn.py
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# 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 miplearn import KnnWarmStartPredictor
from sklearn.metrics import accuracy_score, precision_score
import numpy as np
def test_knn_with_consensus():
x_train = np.array([
[0.0, 0.0],
[0.1, 0.0],
[0.0, 0.1],
[1.0, 1.0],
])
y_train = np.array([
[0., 1.],
[0., 1.],
[0., 1.],
[1., 0.],
])
ws = KnnWarmStartPredictor(k=3, thr_clip=[0.75, 0.75])
ws.fit(x_train, y_train)
x_test = np.array([[0.0, 0.0]])
y_test = np.array([[0, 1]])
assert (ws.predict(x_test) == y_test).all()
def test_knn_without_consensus():
x_train = np.array([
[0.0, 0.0],
[0.1, 0.1],
[0.9, 0.9],
[1.0, 1.0],
])
y_train = np.array([
[0., 1.],
[0., 1.],
[1., 0.],
[1., 0.],
])
ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
ws.fit(x_train, y_train)
x_test = np.array([[0.5, 0.5]])
y_test = np.array([[0, 0]])
assert (ws.predict(x_test) == y_test).all()
def test_knn_always_true():
x_train = np.array([
[0.0, 0.0],
[0.1, 0.1],
[0.9, 0.9],
[1.0, 1.0],
])
y_train = np.array([
[1., 0.],
[1., 0.],
[1., 0.],
[1., 0.],
])
ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
ws.fit(x_train, y_train)
x_test = np.array([[0.5, 0.5]])
y_test = np.array([[1, 0]])
assert (ws.predict(x_test) == y_test).all()
def test_knn_always_false():
x_train = np.array([
[0.0, 0.0],
[0.1, 0.1],
[0.9, 0.9],
[1.0, 1.0],
])
y_train = np.array([
[0., 1.],
[0., 1.],
[0., 1.],
[0., 1.],
])
ws = KnnWarmStartPredictor(k=4, thr_clip=[0.75, 0.75])
ws.fit(x_train, y_train)
x_test = np.array([[0.5, 0.5]])
y_test = np.array([[0, 1]])
assert (ws.predict(x_test) == y_test).all()

64
miplearn/components/tests/test_warmstart_logistic.py
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# 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 miplearn import LogisticWarmStartPredictor
from sklearn.metrics import accuracy_score, precision_score
import numpy as np
def _generate_dataset(ground_truth, n_samples=10_000):
x_train = np.random.rand(n_samples,5)
x_test = np.random.rand(n_samples,5)
y_train = ground_truth(x_train)
y_test = ground_truth(x_test)
return x_train, y_train, x_test, y_test
def _is_sum_greater_than_two(x):
y = (np.sum(x, axis=1) > 2.0).astype(int)
return np.vstack([y, 1 - y]).transpose()
def _always_zero(x):
y = np.zeros((1, x.shape[0]))
return np.vstack([y, 1 - y]).transpose()
def _random_values(x):
y = np.random.randint(2, size=x.shape[0])
return np.vstack([y, 1 - y]).transpose()
def test_logistic_ws_with_balanced_labels():
x_train, y_train, x_test, y_test = _generate_dataset(_is_sum_greater_than_two)
ws = LogisticWarmStartPredictor()
ws.fit(x_train, y_train)
y_pred = ws.predict(x_test)
assert accuracy_score(y_test[:,0], y_pred[:,0]) > 0.99
assert accuracy_score(y_test[:,1], y_pred[:,1]) > 0.99
def test_logistic_ws_with_unbalanced_labels():
x_train, y_train, x_test, y_test = _generate_dataset(_always_zero)
ws = LogisticWarmStartPredictor()
ws.fit(x_train, y_train)
y_pred = ws.predict(x_test)
assert accuracy_score(y_test[:,0], y_pred[:,0]) == 1.0
assert accuracy_score(y_test[:,1], y_pred[:,1]) == 1.0
def test_logistic_ws_with_unpredictable_labels():
x_train, y_train, x_test, y_test = _generate_dataset(_random_values)
ws = LogisticWarmStartPredictor()
ws.fit(x_train, y_train)
y_pred = ws.predict(x_test)
assert np.sum(y_pred) == 0
def test_logistic_ws_with_small_sample_size():
x_train, y_train, x_test, y_test = _generate_dataset(_random_values, n_samples=3)
ws = LogisticWarmStartPredictor()
ws.fit(x_train, y_train)
y_pred = ws.predict(x_test)
assert np.sum(y_pred) == 0