75 lines
2.5 KiB
Python
75 lines
2.5 KiB
Python
# Author: Olivier Grisel <olivier.grisel@ensta.org>
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#
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# License: BSD 3 clause
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import numpy as np
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from numpy.testing import assert_array_almost_equal, assert_array_equal
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from sklearn.utils.murmurhash import murmurhash3_32
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def test_mmhash3_int():
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assert murmurhash3_32(3) == 847579505
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assert murmurhash3_32(3, seed=0) == 847579505
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assert murmurhash3_32(3, seed=42) == -1823081949
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assert murmurhash3_32(3, positive=False) == 847579505
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assert murmurhash3_32(3, seed=0, positive=False) == 847579505
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assert murmurhash3_32(3, seed=42, positive=False) == -1823081949
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assert murmurhash3_32(3, positive=True) == 847579505
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assert murmurhash3_32(3, seed=0, positive=True) == 847579505
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assert murmurhash3_32(3, seed=42, positive=True) == 2471885347
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def test_mmhash3_int_array():
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rng = np.random.RandomState(42)
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keys = rng.randint(-5342534, 345345, size=3 * 2 * 1).astype(np.int32)
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keys = keys.reshape((3, 2, 1))
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for seed in [0, 42]:
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expected = np.array([murmurhash3_32(int(k), seed) for k in keys.flat])
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expected = expected.reshape(keys.shape)
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assert_array_equal(murmurhash3_32(keys, seed), expected)
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for seed in [0, 42]:
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expected = np.array([murmurhash3_32(k, seed, positive=True) for k in keys.flat])
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expected = expected.reshape(keys.shape)
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assert_array_equal(murmurhash3_32(keys, seed, positive=True), expected)
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def test_mmhash3_bytes():
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assert murmurhash3_32(b"foo", 0) == -156908512
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assert murmurhash3_32(b"foo", 42) == -1322301282
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assert murmurhash3_32(b"foo", 0, positive=True) == 4138058784
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assert murmurhash3_32(b"foo", 42, positive=True) == 2972666014
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def test_mmhash3_unicode():
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assert murmurhash3_32("foo", 0) == -156908512
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assert murmurhash3_32("foo", 42) == -1322301282
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assert murmurhash3_32("foo", 0, positive=True) == 4138058784
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assert murmurhash3_32("foo", 42, positive=True) == 2972666014
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def test_no_collision_on_byte_range():
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previous_hashes = set()
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for i in range(100):
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h = murmurhash3_32(" " * i, 0)
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assert h not in previous_hashes, "Found collision on growing empty string"
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def test_uniform_distribution():
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n_bins, n_samples = 10, 100000
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bins = np.zeros(n_bins, dtype=np.float64)
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for i in range(n_samples):
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bins[murmurhash3_32(i, positive=True) % n_bins] += 1
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means = bins / n_samples
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expected = np.full(n_bins, 1.0 / n_bins)
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assert_array_almost_equal(means / expected, np.ones(n_bins), 2)
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