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import pytest
import numpy as np
from numpy.testing import assert_array_equal, assert_equal
from scipy.stats.contingency import crosstab
@pytest.mark.parametrize('sparse', [False, True])
def test_crosstab_basic(sparse):
a = [0, 0, 9, 9, 0, 0, 9]
b = [2, 1, 3, 1, 2, 3, 3]
expected_avals = [0, 9]
expected_bvals = [1, 2, 3]
expected_count = np.array([[1, 2, 1],
[1, 0, 2]])
(avals, bvals), count = crosstab(a, b, sparse=sparse)
assert_array_equal(avals, expected_avals)
assert_array_equal(bvals, expected_bvals)
if sparse:
assert_array_equal(count.toarray(), expected_count)
else:
assert_array_equal(count, expected_count)
def test_crosstab_basic_1d():
# Verify that a single input sequence works as expected.
x = [1, 2, 3, 1, 2, 3, 3]
expected_xvals = [1, 2, 3]
expected_count = np.array([2, 2, 3])
(xvals,), count = crosstab(x)
assert_array_equal(xvals, expected_xvals)
assert_array_equal(count, expected_count)
def test_crosstab_basic_3d():
# Verify the function for three input sequences.
a = 'a'
b = 'b'
x = [0, 0, 9, 9, 0, 0, 9, 9]
y = [a, a, a, a, b, b, b, a]
z = [1, 2, 3, 1, 2, 3, 3, 1]
expected_xvals = [0, 9]
expected_yvals = [a, b]
expected_zvals = [1, 2, 3]
expected_count = np.array([[[1, 1, 0],
[0, 1, 1]],
[[2, 0, 1],
[0, 0, 1]]])
(xvals, yvals, zvals), count = crosstab(x, y, z)
assert_array_equal(xvals, expected_xvals)
assert_array_equal(yvals, expected_yvals)
assert_array_equal(zvals, expected_zvals)
assert_array_equal(count, expected_count)
@pytest.mark.parametrize('sparse', [False, True])
def test_crosstab_levels(sparse):
a = [0, 0, 9, 9, 0, 0, 9]
b = [1, 2, 3, 1, 2, 3, 3]
expected_avals = [0, 9]
expected_bvals = [0, 1, 2, 3]
expected_count = np.array([[0, 1, 2, 1],
[0, 1, 0, 2]])
(avals, bvals), count = crosstab(a, b, levels=[None, [0, 1, 2, 3]],
sparse=sparse)
assert_array_equal(avals, expected_avals)
assert_array_equal(bvals, expected_bvals)
if sparse:
assert_array_equal(count.toarray(), expected_count)
else:
assert_array_equal(count, expected_count)
@pytest.mark.parametrize('sparse', [False, True])
def test_crosstab_extra_levels(sparse):
# The pair of values (-1, 3) will be ignored, because we explicitly
# request the counted `a` values to be [0, 9].
a = [0, 0, 9, 9, 0, 0, 9, -1]
b = [1, 2, 3, 1, 2, 3, 3, 3]
expected_avals = [0, 9]
expected_bvals = [0, 1, 2, 3]
expected_count = np.array([[0, 1, 2, 1],
[0, 1, 0, 2]])
(avals, bvals), count = crosstab(a, b, levels=[[0, 9], [0, 1, 2, 3]],
sparse=sparse)
assert_array_equal(avals, expected_avals)
assert_array_equal(bvals, expected_bvals)
if sparse:
assert_array_equal(count.toarray(), expected_count)
else:
assert_array_equal(count, expected_count)
def test_validation_at_least_one():
with pytest.raises(TypeError, match='At least one'):
crosstab()
def test_validation_same_lengths():
with pytest.raises(ValueError, match='must have the same length'):
crosstab([1, 2], [1, 2, 3, 4])
def test_validation_sparse_only_two_args():
with pytest.raises(ValueError, match='only two input sequences'):
crosstab([0, 1, 1], [8, 8, 9], [1, 3, 3], sparse=True)
def test_validation_len_levels_matches_args():
with pytest.raises(ValueError, match='number of input sequences'):
crosstab([0, 1, 1], [8, 8, 9], levels=([0, 1, 2, 3],))
def test_result():
res = crosstab([0, 1], [1, 2])
assert_equal((res.elements, res.count), res)
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