def check_target_type(name, Estimator):
X = np.random.random((20, 2))
y = np.linspace(0, 1, 20)
estimator = Estimator()
set_random_state(estimator)
with warns(UserWarning, match='should be of types'):
estimator.fit(X, y)
def test_sampling_strategy_dict_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
sampling_strategy = {1: 70, 2: 140, 3: 70}
expected_msg = (r"After over-sampling, the number of samples \(140\) in"
r" class 2 will be larger than the number of samples in"
r" the majority class \(class #2 -> 100\)")
with warns(UserWarning, expected_msg):
check_sampling_strategy(sampling_strategy, y, 'over-sampling')
def test_ratio_dict_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = {1: 70, 2: 100, 3: 70}
ratio_ = check_ratio(ratio, y, 'over-sampling')
assert ratio_ == {1: 20, 2: 0, 3: 45}
ratio = {1: 70, 2: 140, 3: 70}
expected_msg = ("After over-sampling, the number of samples \(140\) in"
" class 2 will be larger than the number of samples in the"
" majority class \(class #2 -> 100\)")
with warns(UserWarning, expected_msg):
check_ratio(ratio, y, 'over-sampling')
def test_warns():
import warnings
with warns(UserWarning, match=r'must be \d+$'):
warnings.warn("value must be 42", UserWarning)
with raises(AssertionError, match='pattern not found'):
with warns(UserWarning, match=r'must be \d+$'):
warnings.warn("this is not here", UserWarning)
with warns(UserWarning, match=r'aaa'):
warnings.warn("cccccccccc", UserWarning)
warnings.warn("bbbbbbbbbb", UserWarning)
warnings.warn("aaaaaaaaaa", UserWarning)
a, b, c = ('aaa', 'bbbbbbbbbb', 'cccccccccc')
expected_msg = "'{}' pattern not found in \['{}', '{}'\]".format(a, b, c)
with raises(AssertionError, match=expected_msg):
with warns(UserWarning, match=r'aaa'):
warnings.warn("bbbbbbbbbb", UserWarning)
warnings.warn("cccccccccc", UserWarning)
def test_sensitivity_specificity_unused_pos_label():
# but average != 'binary'; even if data is binary
with warns(UserWarning, "use labels=\[pos_label\] to specify a single"):
sensitivity_specificity_support(
[1, 2, 1], [1, 2, 2], pos_label=2, average='macro')
def test_deprecation_random_state():
tl = TomekLinks(random_state=0)
with warns(
DeprecationWarning, match="'random_state' is deprecated from 0.4"):
tl.fit_resample(X, Y)
def test_deprecation_random_state():
renn = RepeatedEditedNearestNeighbours(random_state=0)
with warns(DeprecationWarning,
match="'random_state' is deprecated from 0.4"):
renn.fit_sample(X, Y)
def test_deprecation_random_state():
nm = NearMiss(random_state=0)
with warns(
DeprecationWarning, match="'random_state' is deprecated from 0.4"):
nm.fit_resample(X, Y)
def test_deprecation_random_state():
allknn = AllKNN(random_state=0)
with warns(
DeprecationWarning, match="'random_state' is deprecated from 0.4"):
allknn.fit_resample(X, Y)
def test_deprecation_random_state():
allknn = AllKNN(random_state=0)
with warns(DeprecationWarning,
match="'random_state' is deprecated from 0.4"):
allknn.fit_resample(X, Y)
def test_deprecation_random_state():
ncr = NeighbourhoodCleaningRule(random_state=0)
with warns(
DeprecationWarning, match="'random_state' is deprecated from 0.4"):
ncr.fit_resample(X, Y)
def test_deprecation_random_state():
renn = RepeatedEditedNearestNeighbours(random_state=0)
with warns(DeprecationWarning,
match="'random_state' is deprecated from 0.4"):
renn.fit_resample(X, Y)
def test_deprecation_random_state():
ncr = NeighbourhoodCleaningRule(random_state=0)
with warns(
DeprecationWarning, match="'random_state' is deprecated from 0.4"):
ncr.fit_resample(X, Y)
def test_deprecation_random_state():
tl = TomekLinks(random_state=0)
with warns(DeprecationWarning,
match="'random_state' is deprecated from 0.4"):
tl.fit_resample(X, Y)
def test_sensitivity_specificity_unused_pos_label():
# but average != 'binary'; even if data is binary
with warns(UserWarning, "use labels=\[pos_label\] to specify a single"):
sensitivity_specificity_support([1, 2, 1], [1, 2, 2],
pos_label=2,
average='macro')
def test_deprecate_parameter():
with warns(DeprecationWarning, match="is deprecated from"):
deprecate_parameter(Sampler(), '0.2', 'a')
with warns(DeprecationWarning, match="Use 'b' instead."):
deprecate_parameter(Sampler(), '0.2', 'a', 'b')