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194 lines
6.4 KiB
Python
194 lines
6.4 KiB
Python
from __future__ import division, print_function, absolute_import
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import numpy as np
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from numpy.testing import TestCase, run_module_suite, assert_equal, \
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assert_array_equal
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from wafo.stats import rankdata, tiecorrect
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class TestTieCorrect(TestCase):
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def test_empty(self):
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"""An empty array requires no correction, should return 1.0."""
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ranks = np.array([], dtype=np.float64)
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c = tiecorrect(ranks)
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assert_equal(c, 1.0)
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def test_one(self):
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"""A single element requires no correction, should return 1.0."""
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ranks = np.array([1.0], dtype=np.float64)
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c = tiecorrect(ranks)
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assert_equal(c, 1.0)
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def test_no_correction(self):
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"""Arrays with no ties require no correction."""
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ranks = np.arange(2.0)
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c = tiecorrect(ranks)
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assert_equal(c, 1.0)
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ranks = np.arange(3.0)
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c = tiecorrect(ranks)
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assert_equal(c, 1.0)
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def test_basic(self):
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"""Check a few basic examples of the tie correction factor."""
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# One tie of two elements
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ranks = np.array([1.0, 2.5, 2.5])
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c = tiecorrect(ranks)
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T = 2.0
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N = ranks.size
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expected = 1.0 - (T**3 - T) / (N**3 - N)
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assert_equal(c, expected)
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# One tie of two elements (same as above, but tie is not at the end)
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ranks = np.array([1.5, 1.5, 3.0])
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c = tiecorrect(ranks)
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T = 2.0
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N = ranks.size
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expected = 1.0 - (T**3 - T) / (N**3 - N)
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assert_equal(c, expected)
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# One tie of three elements
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ranks = np.array([1.0, 3.0, 3.0, 3.0])
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c = tiecorrect(ranks)
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T = 3.0
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N = ranks.size
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expected = 1.0 - (T**3 - T) / (N**3 - N)
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assert_equal(c, expected)
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# Two ties, lengths 2 and 3.
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ranks = np.array([1.5, 1.5, 4.0, 4.0, 4.0])
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c = tiecorrect(ranks)
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T1 = 2.0
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T2 = 3.0
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N = ranks.size
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expected = 1.0 - ((T1**3 - T1) + (T2**3 - T2)) / (N**3 - N)
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assert_equal(c, expected)
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class TestRankData(TestCase):
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def test_empty(self):
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"""stats.rankdata([]) should return an empty array."""
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a = np.array([], dtype=np.int)
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r = rankdata(a)
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assert_array_equal(r, np.array([], dtype=np.float64))
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r = rankdata([])
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assert_array_equal(r, np.array([], dtype=np.float64))
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def test_one(self):
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"""Check stats.rankdata with an array of length 1."""
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data = [100]
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a = np.array(data, dtype=np.int)
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r = rankdata(a)
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assert_array_equal(r, np.array([1.0], dtype=np.float64))
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r = rankdata(data)
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assert_array_equal(r, np.array([1.0], dtype=np.float64))
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def test_basic(self):
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"""Basic tests of stats.rankdata."""
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data = [100, 10, 50]
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expected = np.array([3.0, 1.0, 2.0], dtype=np.float64)
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a = np.array(data, dtype=np.int)
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r = rankdata(a)
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assert_array_equal(r, expected)
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r = rankdata(data)
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assert_array_equal(r, expected)
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data = [40, 10, 30, 10, 50]
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expected = np.array([4.0, 1.5, 3.0, 1.5, 5.0], dtype=np.float64)
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a = np.array(data, dtype=np.int)
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r = rankdata(a)
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assert_array_equal(r, expected)
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r = rankdata(data)
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assert_array_equal(r, expected)
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data = [20, 20, 20, 10, 10, 10]
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expected = np.array([5.0, 5.0, 5.0, 2.0, 2.0, 2.0], dtype=np.float64)
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a = np.array(data, dtype=np.int)
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r = rankdata(a)
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assert_array_equal(r, expected)
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r = rankdata(data)
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assert_array_equal(r, expected)
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# The docstring states explicitly that the argument is flattened.
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a2d = a.reshape(2, 3)
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r = rankdata(a2d)
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assert_array_equal(r, expected)
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def test_large_int(self):
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data = np.array([2**60, 2**60+1], dtype=np.uint64)
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r = rankdata(data)
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assert_array_equal(r, [1.0, 2.0])
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data = np.array([2**60, 2**60+1], dtype=np.int64)
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r = rankdata(data)
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assert_array_equal(r, [1.0, 2.0])
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data = np.array([2**60, -2**60+1], dtype=np.int64)
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r = rankdata(data)
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assert_array_equal(r, [2.0, 1.0])
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def test_big_tie(self):
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for n in [10000, 100000, 1000000]:
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data = np.ones(n, dtype=int)
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r = rankdata(data)
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expected_rank = 0.5 * (n + 1)
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assert_array_equal(r, expected_rank * data,
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"test failed with n=%d" % n)
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_cases = (
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# values, method, expected
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([], 'average', []),
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([], 'min', []),
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([], 'max', []),
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([], 'dense', []),
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([], 'ordinal', []),
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#
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([100], 'average', [1.0]),
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([100], 'min', [1.0]),
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([100], 'max', [1.0]),
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([100], 'dense', [1.0]),
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([100], 'ordinal', [1.0]),
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#
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([100, 100, 100], 'average', [2.0, 2.0, 2.0]),
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([100, 100, 100], 'min', [1.0, 1.0, 1.0]),
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([100, 100, 100], 'max', [3.0, 3.0, 3.0]),
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([100, 100, 100], 'dense', [1.0, 1.0, 1.0]),
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([100, 100, 100], 'ordinal', [1.0, 2.0, 3.0]),
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#
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([100, 300, 200], 'average', [1.0, 3.0, 2.0]),
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([100, 300, 200], 'min', [1.0, 3.0, 2.0]),
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([100, 300, 200], 'max', [1.0, 3.0, 2.0]),
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([100, 300, 200], 'dense', [1.0, 3.0, 2.0]),
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([100, 300, 200], 'ordinal', [1.0, 3.0, 2.0]),
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#
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([100, 200, 300, 200], 'average', [1.0, 2.5, 4.0, 2.5]),
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([100, 200, 300, 200], 'min', [1.0, 2.0, 4.0, 2.0]),
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([100, 200, 300, 200], 'max', [1.0, 3.0, 4.0, 3.0]),
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([100, 200, 300, 200], 'dense', [1.0, 2.0, 3.0, 2.0]),
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([100, 200, 300, 200], 'ordinal', [1.0, 2.0, 4.0, 3.0]),
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#
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([100, 200, 300, 200, 100], 'average', [1.5, 3.5, 5.0, 3.5, 1.5]),
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([100, 200, 300, 200, 100], 'min', [1.0, 3.0, 5.0, 3.0, 1.0]),
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([100, 200, 300, 200, 100], 'max', [2.0, 4.0, 5.0, 4.0, 2.0]),
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([100, 200, 300, 200, 100], 'dense', [1.0, 2.0, 3.0, 2.0, 1.0]),
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([100, 200, 300, 200, 100], 'ordinal', [1.0, 3.0, 5.0, 4.0, 2.0]),
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#
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([10] * 30, 'ordinal', np.arange(1.0, 31.0)),
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)
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def test_cases():
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def check_case(values, method, expected):
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r = rankdata(values, method=method)
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assert_array_equal(r, expected)
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for values, method, expected in _cases:
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yield check_case, values, method, expected
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if __name__ == "__main__":
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run_module_suite()
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