projektAI/venv/Lib/site-packages/scipy/special/tests/test_log_softmax.py
2021-06-06 22:13:05 +02:00

110 lines
3.3 KiB
Python

import numpy as np
from numpy.testing import assert_allclose
import pytest
import scipy.special as sc
@pytest.mark.parametrize('x, expected', [
(np.array([1000, 1]), np.array([0, -999])),
# Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
# converted to float.
(np.arange(4), np.array([-3.4401896985611953,
-2.4401896985611953,
-1.4401896985611953,
-0.44018969856119533]))
])
def test_log_softmax(x, expected):
assert_allclose(sc.log_softmax(x), expected, rtol=1e-13)
@pytest.fixture
def log_softmax_x():
x = np.arange(4)
return x
@pytest.fixture
def log_softmax_expected():
# Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
# converted to float.
expected = np.array([-3.4401896985611953,
-2.4401896985611953,
-1.4401896985611953,
-0.44018969856119533])
return expected
def test_log_softmax_translation(log_softmax_x, log_softmax_expected):
# Translation property. If all the values are changed by the same amount,
# the softmax result does not change.
x = log_softmax_x + 100
expected = log_softmax_expected
assert_allclose(sc.log_softmax(x), expected, rtol=1e-13)
def test_log_softmax_noneaxis(log_softmax_x, log_softmax_expected):
# When axis=None, softmax operates on the entire array, and preserves
# the shape.
x = log_softmax_x.reshape(2, 2)
expected = log_softmax_expected.reshape(2, 2)
assert_allclose(sc.log_softmax(x), expected, rtol=1e-13)
@pytest.mark.parametrize('axis_2d, expected_2d', [
(0, np.log(0.5) * np.ones((2, 2))),
(1, np.array([[0, -999], [0, -999]]))
])
def test_axes(axis_2d, expected_2d):
assert_allclose(
sc.log_softmax([[1000, 1], [1000, 1]], axis=axis_2d),
expected_2d,
rtol=1e-13,
)
@pytest.fixture
def log_softmax_2d_x():
x = np.arange(8).reshape(2, 4)
return x
@pytest.fixture
def log_softmax_2d_expected():
# Expected value computed using mpmath (with mpmath.mp.dps = 200) and then
# converted to float.
expected = np.array([[-3.4401896985611953,
-2.4401896985611953,
-1.4401896985611953,
-0.44018969856119533],
[-3.4401896985611953,
-2.4401896985611953,
-1.4401896985611953,
-0.44018969856119533]])
return expected
def test_log_softmax_2d_axis1(log_softmax_2d_x, log_softmax_2d_expected):
x = log_softmax_2d_x
expected = log_softmax_2d_expected
assert_allclose(sc.log_softmax(x, axis=1), expected, rtol=1e-13)
def test_log_softmax_2d_axis0(log_softmax_2d_x, log_softmax_2d_expected):
x = log_softmax_2d_x.T
expected = log_softmax_2d_expected.T
assert_allclose(sc.log_softmax(x, axis=0), expected, rtol=1e-13)
def test_log_softmax_3d(log_softmax_2d_x, log_softmax_2d_expected):
# 3-d input, with a tuple for the axis.
x_3d = log_softmax_2d_x.reshape(2, 2, 2)
expected_3d = log_softmax_2d_expected.reshape(2, 2, 2)
assert_allclose(sc.log_softmax(x_3d, axis=(1, 2)), expected_3d, rtol=1e-13)
def test_log_softmax_scalar():
assert_allclose(sc.log_softmax(1.0), 0.0, rtol=1e-13)