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zeekay
bde2e2531b
Machine Learning with FHE - fork of Zama Concrete-ML - Built-in FHE-friendly ML models - scikit-learn compatible API - PyTorch model conversion - Quantization-aware training
42 lines
1.5 KiB
Python
42 lines
1.5 KiB
Python
"""Test pytest utility functions."""
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import numpy
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import pytest
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from numpy.random import RandomState
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from concrete.ml.pytest.utils import values_are_equal
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@pytest.mark.parametrize(
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"value_1, value_2, expected_output",
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[
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pytest.param(None, None, True),
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pytest.param(None, 0, False),
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pytest.param(numpy.array([0]), numpy.array([0]), True),
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pytest.param(
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numpy.array([0], dtype=numpy.int64), numpy.array([0], dtype=numpy.int32), False
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),
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pytest.param(numpy.array([0]), 0, False),
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pytest.param(numpy.int64(0), numpy.int64(0), True),
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pytest.param(numpy.int64(0), numpy.int32(0), False),
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pytest.param(numpy.int64(0), 0, False),
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pytest.param(numpy.float64(0), numpy.float64(0), True),
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pytest.param(numpy.float64(0), numpy.float32(0), False),
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pytest.param(numpy.float64(0), 0, False),
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pytest.param(RandomState(0), RandomState(0), True),
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pytest.param(RandomState(0), RandomState(1), False),
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pytest.param(RandomState(0), 0, False),
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pytest.param(0, 0, True),
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pytest.param(0, 1, False),
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],
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)
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def test_values_are_equal(value_1, value_2, expected_output):
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"""Check that values_are_equal works properly."""
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assert values_are_equal(value_1, value_2) == expected_output, (
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f"The values_are_equal utility function does not match the expected output: {value_1} and "
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f"{value_2} were expected to be "
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+ "equal " * expected_output
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+ "different " * (1 - expected_output)
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)
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