def test_random_choice_csc(n_samples=10000, random_state=24):
# Explicit class probabilities
classes = [np.array([0, 1]), np.array([0, 1, 2])]
class_probabilities = [np.array([0.5, 0.5]), np.array([0.6, 0.1, 0.3])]
got = _random_choice_csc(n_samples, classes, class_probabilities,
random_state)
assert sp.issparse(got)
for k in range(len(classes)):
p = np.bincount(got.getcol(k).toarray().ravel()) / float(n_samples)
assert_array_almost_equal(class_probabilities[k], p, decimal=1)
# Implicit class probabilities
classes = [[0, 1], [1, 2]] # test for array-like support
class_probabilities = [np.array([0.5, 0.5]), np.array([0, 1 / 2, 1 / 2])]
got = _random_choice_csc(n_samples=n_samples,
classes=classes,
random_state=random_state)
assert sp.issparse(got)
for k in range(len(classes)):
p = np.bincount(got.getcol(k).toarray().ravel()) / float(n_samples)
assert_array_almost_equal(class_probabilities[k], p, decimal=1)
# Edge case probabilities 1.0 and 0.0
classes = [np.array([0, 1]), np.array([0, 1, 2])]
class_probabilities = [np.array([1.0, 0.0]), np.array([0.0, 1.0, 0.0])]
got = _random_choice_csc(n_samples, classes, class_probabilities,
random_state)
assert sp.issparse(got)
for k in range(len(classes)):
p = np.bincount(got.getcol(k).toarray().ravel(),
minlength=len(class_probabilities[k])) / n_samples
assert_array_almost_equal(class_probabilities[k], p, decimal=1)
# One class target data
classes = [[1], [0]] # test for array-like support
class_probabilities = [np.array([0.0, 1.0]), np.array([1.0])]
got = _random_choice_csc(n_samples=n_samples,
classes=classes,
random_state=random_state)
assert sp.issparse(got)
for k in range(len(classes)):
p = np.bincount(got.getcol(k).toarray().ravel()) / n_samples
assert_array_almost_equal(class_probabilities[k], p, decimal=1)
def test_random_choice_csc_errors():
# the length of an array in classes and class_probabilities is mismatched
classes = [np.array([0, 1]), np.array([0, 1, 2, 3])]
class_probabilities = [np.array([0.5, 0.5]), np.array([0.6, 0.1, 0.3])]
with pytest.raises(ValueError):
_random_choice_csc(4, classes, class_probabilities, 1)
# the class dtype is not supported
classes = [np.array(["a", "1"]), np.array(["z", "1", "2"])]
class_probabilities = [np.array([0.5, 0.5]), np.array([0.6, 0.1, 0.3])]
with pytest.raises(ValueError):
_random_choice_csc(4, classes, class_probabilities, 1)
# the class dtype is not supported
classes = [np.array([4.2, 0.1]), np.array([0.1, 0.2, 9.4])]
class_probabilities = [np.array([0.5, 0.5]), np.array([0.6, 0.1, 0.3])]
with pytest.raises(ValueError):
_random_choice_csc(4, classes, class_probabilities, 1)
# Given probabilities don't sum to 1
classes = [np.array([0, 1]), np.array([0, 1, 2])]
class_probabilities = [np.array([0.5, 0.6]), np.array([0.6, 0.1, 0.3])]
with pytest.raises(ValueError):
_random_choice_csc(4, classes, class_probabilities, 1)
