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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
77 lines
2.8 KiB
Python
77 lines
2.8 KiB
Python
"""Tests for our inputs checks"""
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import numpy
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import pytest
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from concrete.ml.common.check_inputs import check_array_and_assert, check_X_y_and_assert
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x_with_nans = [numpy.array([[numpy.nan], [0.0]]), numpy.array([[numpy.nan], [numpy.nan]])]
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x_clean = [numpy.array([[1.0], [2.0]]), numpy.array([[0], [10]])]
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x_1_element = [numpy.array([[2.0]])]
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y_1_element = [numpy.array([2.0])]
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y_with_nans = [numpy.array([numpy.nan, 0.0]), numpy.array([numpy.nan, numpy.nan])]
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y_clean = [numpy.array([1.0, 2.0]), numpy.array([0, 10])]
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@pytest.mark.parametrize("x", x_with_nans)
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def test_check_array_and_assert_nan(x):
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"""Checks that with an input containing a Nan the function raises an error"""
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with pytest.raises(ValueError, match=r"Input .*contains NaN.*"):
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check_array_and_assert(x)
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@pytest.mark.parametrize("x", x_clean)
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def test_check_array_and_assert_clean(x):
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"""Checks that with a correct input no change is done to it"""
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checked_x = check_array_and_assert(x)
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assert numpy.array_equal(checked_x, x)
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@pytest.mark.parametrize("x", x_with_nans)
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@pytest.mark.parametrize("y", y_clean)
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def test_check_x_y_and_assert_nan_clean(x, y):
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"""Checks that if X contains a Nan the function raises an error"""
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with pytest.raises(ValueError, match=r"Input .*contains NaN.*"):
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check_X_y_and_assert(x, y)
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@pytest.mark.parametrize("x", x_clean)
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@pytest.mark.parametrize("y", y_with_nans)
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def test_check_x_y_and_assert_clean_nan(x, y):
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"""Checks that if y contains a Nan the function raises an error"""
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with pytest.raises(ValueError, match=r"Input .*contains NaN.*"):
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check_X_y_and_assert(x, y)
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@pytest.mark.parametrize("x", x_with_nans)
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@pytest.mark.parametrize("y", y_with_nans)
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def test_check_x_y_and_assert_nan_nan(x, y):
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"""Checks that if X and y contain Nan values the function raises an error"""
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with pytest.raises(ValueError, match=r"Input .*contains NaN.*"):
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check_X_y_and_assert(x, y)
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@pytest.mark.parametrize("x", x_clean)
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@pytest.mark.parametrize("y", y_clean)
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def test_check_x_y_and_assert_clean_clean(x, y):
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"""Checks that with a correct input no change is done to it"""
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checked_x, checked_y = check_X_y_and_assert(x, y)
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assert numpy.array_equal(checked_x, x)
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assert numpy.array_equal(checked_y, y)
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@pytest.mark.parametrize("x", x_1_element)
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@pytest.mark.parametrize("y", y_clean)
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def test_check_x_y_and_assert_1elt_clean(x, y):
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"""Checks that if X and y don't have the same length the function raises an error"""
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with pytest.raises(ValueError):
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check_X_y_and_assert(x, y)
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@pytest.mark.parametrize("x", x_clean)
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@pytest.mark.parametrize("y", y_1_element)
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def test_check_x_y_and_assert_clean_1elt(x, y):
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"""Checks that if X and y don't have the same length the function raises an error"""
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with pytest.raises(ValueError):
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check_X_y_and_assert(x, y)
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