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79 lines
2.7 KiB
79 lines
2.7 KiB
# 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 tempfile import NamedTemporaryFile
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from typing import Any
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import numpy as np
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from scipy.sparse import coo_matrix
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from miplearn.features.sample import MemorySample, Sample, Hdf5Sample
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def test_memory_sample() -> None:
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_test_sample(MemorySample())
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def test_hdf5_sample() -> None:
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file = NamedTemporaryFile()
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_test_sample(Hdf5Sample(file.name))
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def _test_sample(sample: Sample) -> None:
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_assert_roundtrip_scalar(sample, "A")
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_assert_roundtrip_scalar(sample, True)
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_assert_roundtrip_scalar(sample, 1)
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_assert_roundtrip_scalar(sample, 1.0)
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assert sample.get_scalar("unknown-key") is None
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_assert_roundtrip_array(sample, np.array([True, False], dtype="bool"))
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_assert_roundtrip_array(sample, np.array([1, 2, 3], dtype="int16"))
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_assert_roundtrip_array(sample, np.array([1, 2, 3], dtype="int32"))
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_assert_roundtrip_array(sample, np.array([1, 2, 3], dtype="int64"))
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_assert_roundtrip_array(sample, np.array([1.0, 2.0, 3.0], dtype="float16"))
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_assert_roundtrip_array(sample, np.array([1.0, 2.0, 3.0], dtype="float32"))
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_assert_roundtrip_array(sample, np.array([1.0, 2.0, 3.0], dtype="float64"))
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_assert_roundtrip_array(sample, np.array(["A", "BB", "CCC"], dtype="S"))
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assert sample.get_array("unknown-key") is None
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_assert_roundtrip_sparse(
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sample,
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coo_matrix(
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[
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[1, 0, 0],
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[0, 2, 3],
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[0, 0, 4],
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],
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dtype=float,
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),
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)
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assert sample.get_sparse("unknown-key") is None
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def _assert_roundtrip_array(sample: Sample, original: np.ndarray) -> None:
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sample.put_array("key", original)
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recovered = sample.get_array("key")
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assert recovered is not None
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assert isinstance(recovered, np.ndarray)
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assert recovered.dtype == original.dtype
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assert (recovered == original).all()
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def _assert_roundtrip_scalar(sample: Sample, original: Any) -> None:
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sample.put_scalar("key", original)
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recovered = sample.get_scalar("key")
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assert recovered == original
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assert recovered is not None
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assert isinstance(
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recovered, original.__class__
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), f"Expected {original.__class__}, found {recovered.__class__} instead"
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def _assert_roundtrip_sparse(sample: Sample, original: coo_matrix) -> None:
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sample.put_sparse("key", original)
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recovered = sample.get_sparse("key")
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assert recovered is not None
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assert isinstance(recovered, coo_matrix)
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assert recovered.dtype == original.dtype
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assert (original != recovered).sum() == 0
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