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RelaxationComponent: Always use np arrays

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Alinson S. Xavier
2021-01-07 12:29:43 -06:00
parent 4057a65506
commit 3f1aec7fad
2 changed files with 64 additions and 39 deletions
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5
miplearn/components/steps/drop_redundant.py
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@@ -5,6 +5,7 @@
import logging import logging
from copy import deepcopy from copy import deepcopy
import numpy as np
from tqdm import tqdm from tqdm import tqdm
from miplearn import Component from miplearn import Component
@@ -101,6 +102,8 @@ class DropRedundantInequalitiesStep(Component):
constraints[category] = [] constraints[category] = []
x[category] += [instance.get_constraint_features(cid)] x[category] += [instance.get_constraint_features(cid)]
constraints[category] += [cid] constraints[category] += [cid]
for category in x.keys():
x[category] = np.array(x[category])
if return_constraints: if return_constraints:
return x, constraints return x, constraints
else: else:
@@ -131,7 +134,7 @@ class DropRedundantInequalitiesStep(Component):
if category not in self.classifiers: if category not in self.classifiers:
continue continue
y[category] = [] y[category] = []
# x_cat = np.array(x_cat) x_cat = np.array(x_cat)
proba = self.classifiers[category].predict_proba(x_cat) proba = self.classifiers[category].predict_proba(x_cat)
for i in range(len(proba)): for i in range(len(proba)):
if proba[i][1] >= self.threshold: if proba[i][1] >= self.threshold:

98
miplearn/components/tests/test_relaxation.py
View File

@@ -2,6 +2,7 @@
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved. # Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details. # Released under the modified BSD license. See COPYING.md for more details.
import numpy as np
from unittest.mock import Mock, call from unittest.mock import Mock, call
from miplearn import LearningSolver, Instance, InternalSolver from miplearn import LearningSolver, Instance, InternalSolver
@@ -28,9 +29,9 @@ def _setup():
instance = Mock(spec=Instance) instance = Mock(spec=Instance)
instance.get_constraint_features = Mock( instance.get_constraint_features = Mock(
side_effect=lambda cid: { side_effect=lambda cid: {
"c2": [1.0, 0.0], "c2": np.array([1.0, 0.0]),
"c3": [0.5, 0.5], "c3": np.array([0.5, 0.5]),
"c4": [1.0], "c4": np.array([1.0]),
}[cid] }[cid]
) )
instance.get_constraint_category = Mock( instance.get_constraint_category = Mock(
@@ -47,15 +48,19 @@ def _setup():
"type-b": Mock(spec=Classifier), "type-b": Mock(spec=Classifier),
} }
classifiers["type-a"].predict_proba = Mock( classifiers["type-a"].predict_proba = Mock(
return_value=[ return_value=np.array(
[0.20, 0.80], [
[0.05, 0.95], [0.20, 0.80],
] [0.05, 0.95],
]
)
) )
classifiers["type-b"].predict_proba = Mock( classifiers["type-b"].predict_proba = Mock(
return_value=[ return_value=np.array(
[0.02, 0.98], [
] [0.02, 0.98],
]
)
) )
return solver, internal, instance, classifiers return solver, internal, instance, classifiers
@@ -95,10 +100,10 @@ def test_drop_redundant():
) )
# Should ask ML to predict whether constraint should be removed # Should ask ML to predict whether constraint should be removed
component.classifiers["type-a"].predict_proba.assert_called_once_with( type_a_actual = component.classifiers["type-a"].predict_proba.call_args[0][0]
[[1.0, 0.0], [0.5, 0.5]] type_b_actual = component.classifiers["type-b"].predict_proba.call_args[0][0]
) np.testing.assert_array_equal(type_a_actual, np.array([[1.0, 0.0], [0.5, 0.5]]))
component.classifiers["type-b"].predict_proba.assert_called_once_with([[1.0]]) np.testing.assert_array_equal(type_b_actual, np.array([[1.0]]))
# Should ask internal solver to remove constraints predicted as redundant # Should ask internal solver to remove constraints predicted as redundant
assert internal.extract_constraint.call_count == 2 assert internal.extract_constraint.call_count == 2
@@ -176,14 +181,16 @@ def test_x_y_fit_predict_evaluate():
} }
component.classifiers["type-a"].predict_proba = Mock( component.classifiers["type-a"].predict_proba = Mock(
return_value=[ return_value=[
[0.20, 0.80], np.array([0.20, 0.80]),
] ]
) )
component.classifiers["type-b"].predict_proba = Mock( component.classifiers["type-b"].predict_proba = Mock(
return_value=[ return_value=np.array(
[0.50, 0.50], [
[0.05, 0.95], [0.50, 0.50],
] [0.05, 0.95],
]
)
) )
# First mock instance # First mock instance
@@ -203,9 +210,9 @@ def test_x_y_fit_predict_evaluate():
) )
instances[0].get_constraint_features = Mock( instances[0].get_constraint_features = Mock(
side_effect=lambda cid: { side_effect=lambda cid: {
"c2": [1.0, 0.0], "c2": np.array([1.0, 0.0]),
"c3": [0.5, 0.5], "c3": np.array([0.5, 0.5]),
"c4": [1.0], "c4": np.array([1.0]),
}[cid] }[cid]
) )
@@ -226,32 +233,47 @@ def test_x_y_fit_predict_evaluate():
) )
instances[1].get_constraint_features = Mock( instances[1].get_constraint_features = Mock(
side_effect=lambda cid: { side_effect=lambda cid: {
"c3": [0.3, 0.4], "c3": np.array([0.3, 0.4]),
"c4": [0.7], "c4": np.array([0.7]),
"c5": [0.8], "c5": np.array([0.8]),
}[cid] }[cid]
) )
expected_x = { expected_x = {
"type-a": [[1.0, 0.0], [0.5, 0.5], [0.3, 0.4]], "type-a": np.array(
"type-b": [[1.0], [0.7], [0.8]], [
[1.0, 0.0],
[0.5, 0.5],
[0.3, 0.4],
]
),
"type-b": np.array(
[
[1.0],
[0.7],
[0.8],
]
),
}
expected_y = {
"type-a": np.array([[0], [0], [1]]),
"type-b": np.array([[1], [0], [0]]),
} }
expected_y = {"type-a": [[0], [0], [1]], "type-b": [[1], [0], [0]]}
# Should build X and Y matrices correctly # Should build X and Y matrices correctly
assert component.x(instances) == expected_x actual_x = component.x(instances)
assert component.y(instances) == expected_y actual_y = component.y(instances)
for category in ["type-a", "type-b"]:
np.testing.assert_array_equal(actual_x[category], expected_x[category])
np.testing.assert_array_equal(actual_y[category], expected_y[category])
# Should pass along X and Y matrices to classifiers # Should pass along X and Y matrices to classifiers
component.fit(instances) component.fit(instances)
component.classifiers["type-a"].fit.assert_called_with( for category in ["type-a", "type-b"]:
expected_x["type-a"], actual_x = component.classifiers[category].fit.call_args[0][0]
expected_y["type-a"], actual_y = component.classifiers[category].fit.call_args[0][1]
) np.testing.assert_array_equal(actual_x, expected_x[category])
component.classifiers["type-b"].fit.assert_called_with( np.testing.assert_array_equal(actual_y, expected_y[category])
expected_x["type-b"],
expected_y["type-b"],
)
assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]} assert component.predict(expected_x) == {"type-a": [[1]], "type-b": [[0], [1]]}