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102 lines
2.6 KiB
Python
102 lines
2.6 KiB
Python
# MIPLearn: Extensible Framework for Learning-Enhanced Mixed-Integer Optimization
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# Copyright (C) 2020-2021, UChicago Argonne, LLC. All rights reserved.
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# Released under the modified BSD license. See COPYING.md for more details.
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from typing import Dict, Tuple
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from unittest.mock import Mock
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from miplearn.components.component import Component
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from miplearn.features import Features, TrainingSample
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from miplearn.instance.base import Instance
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def test_xy_instance() -> None:
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def _sample_xy(features: Features, sample: str) -> Tuple[Dict, Dict]:
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x = {
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"s1": {
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"category_a": [
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[1, 2, 3],
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[3, 4, 6],
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],
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"category_b": [
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[7, 8, 9],
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],
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},
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"s2": {
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"category_a": [
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[0, 0, 0],
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[0, 5, 3],
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[2, 2, 0],
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],
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"category_c": [
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[0, 0, 0],
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[0, 0, 1],
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],
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},
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"s3": {
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"category_c": [
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[1, 1, 1],
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],
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},
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}
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y = {
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"s1": {
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"category_a": [[1], [2]],
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"category_b": [[3]],
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},
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"s2": {
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"category_a": [[4], [5], [6]],
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"category_c": [[8], [9], [10]],
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},
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"s3": {
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"category_c": [[11]],
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},
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}
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return x[sample], y[sample]
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comp = Component()
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instance_1 = Mock(spec=Instance)
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instance_1.training_data = ["s1", "s2"]
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instance_1.features = {}
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instance_2 = Mock(spec=Instance)
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instance_2.training_data = ["s3"]
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instance_2.features = {}
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comp.sample_xy = _sample_xy # type: ignore
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x_expected = {
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"category_a": [
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[1, 2, 3],
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[3, 4, 6],
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[0, 0, 0],
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[0, 5, 3],
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[2, 2, 0],
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],
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"category_b": [
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[7, 8, 9],
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],
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"category_c": [
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[0, 0, 0],
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[0, 0, 1],
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[1, 1, 1],
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],
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}
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y_expected = {
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"category_a": [
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[1],
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[2],
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[4],
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[5],
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[6],
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],
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"category_b": [
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[3],
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],
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"category_c": [
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[8],
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[9],
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[10],
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[11],
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],
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}
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x_actual, y_actual = comp.xy_instances([instance_1, instance_2])
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assert x_actual == x_expected
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assert y_actual == y_expected
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