def test_ratio_float_error():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = -10
error_regex = "When 'ratio' is a float, it should in the range"
with raises(ValueError, match=error_regex):
check_ratio(ratio, y, 'under-sampling')
ratio = 10
with raises(ValueError, match=error_regex):
check_ratio(ratio, y, 'under-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_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 fit(self, X, y):
"""Find the classes statistics before to perform sampling.
Parameters
----------
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Matrix containing the data which have to be sampled.
y : array-like, shape (n_samples,)
Corresponding label for each sample in X.
Returns
-------
self : object,
Return self.
"""
X, y = check_X_y(X, y, accept_sparse=['csr', 'csc'])
y = check_target_type(y)
self.X_hash_, self.y_hash_ = hash_X_y(X, y)
self.ratio_ = check_ratio(self.ratio, y, self._sampling_type)
# Cluster input space
self.clustering_labels_ = self.clusterer[0][1].fit_predict(X, y)
# Identify majority and minority
majority_label = [label for label, n_samples in self.ratio_.items() if n_samples == 0][0]
minority_labels = [label for label in self.ratio_.keys() if label != majority_label]
# Clusters imbalance ratios
weights = pd.DataFrame()
return self
def test_ratio_callable():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
def ratio_func(y):
# this function could create an equal number of samples
target_stats = Counter(y)
n_samples = max(target_stats.values())
return {key: int(n_samples) for key in target_stats.keys()}
ratio_ = check_ratio(ratio_func, y, 'over-sampling')
assert ratio_ == {1: 50, 2: 0, 3: 75}
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_equal(ratio_, {1: 20, 2: 0, 3: 45})
ratio = {1: 70, 2: 140, 3: 70}
assert_warns_message(
UserWarning, "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)", check_ratio, ratio, y, 'over-sampling')
def test_ratio_callable():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
def ratio_func(y):
# this function could create an equal number of samples
target_stats = Counter(y)
n_samples = max(target_stats.values())
return {key: int(n_samples)
for key in target_stats.keys()}
ratio_ = check_ratio(ratio_func, y, 'over-sampling')
assert ratio_ == {1: 50, 2: 0, 3: 75}
def test_ratio_callable_args():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
multiplier = {1: 1.5, 2: 1, 3: 3}
def ratio_func(y, multiplier):
"""samples such that each class will be affected by the multiplier."""
target_stats = Counter(y)
return {key: int(values * multiplier[key])
for key, values in target_stats.items()}
ratio_ = check_ratio(ratio_func, y, 'over-sampling',
multiplier=multiplier)
assert ratio_ == {1: 25, 2: 0, 3: 50}
def test_ratio_callable_args():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
multiplier = {1: 1.5, 2: 1, 3: 3}
def ratio_func(y, multiplier):
"""samples such that each class will be affected by the multiplier."""
target_stats = Counter(y)
return {
key: int(values * multiplier[key])
for key, values in target_stats.items()
}
ratio_ = check_ratio(ratio_func, y, 'over-sampling', multiplier=multiplier)
assert ratio_ == {1: 25, 2: 0, 3: 50}
def test_check_ratio_error():
with raises(ValueError, match="'sampling_type' should be one of"):
check_ratio('auto', np.array([1, 2, 3]), 'rnd')
error_regex = "The target 'y' needs to have more than 1 class."
with raises(ValueError, match=error_regex):
check_ratio('auto', np.ones((10, )), 'over-sampling')
error_regex = "When 'ratio' is a string, it needs to be one of"
with raises(ValueError, match=error_regex):
check_ratio('rnd', np.array([1, 2, 3]), 'over-sampling')
def test_check_ratio_error():
with raises(ValueError, match="'sampling_type' should be one of"):
check_ratio('auto', np.array([1, 2, 3]), 'rnd')
error_regex = "The target 'y' needs to have more than 1 class."
with raises(ValueError, match=error_regex):
check_ratio('auto', np.ones((10, )), 'over-sampling')
error_regex = "When 'ratio' is a string, it needs to be one of"
with raises(ValueError, match=error_regex):
check_ratio('rnd', np.array([1, 2, 3]), 'over-sampling')
def test_ratio_dict_error():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = {1: -100, 2: 50, 3: 25}
with raises(ValueError, match="in a class cannot be negative."):
check_ratio(ratio, y, 'under-sampling')
ratio = {10: 10}
with raises(ValueError, match="are not present in the data."):
check_ratio(ratio, y, 'over-sampling')
ratio = {1: 45, 2: 100, 3: 70}
error_regex = ("With over-sampling methods, the number of samples in a"
" class should be greater or equal to the original number"
" of samples. Originally, there is 50 samples and 45"
" samples are asked.")
with raises(ValueError, match=error_regex):
check_ratio(ratio, y, 'over-sampling')
error_regex = ("With under-sampling methods, the number of samples in a"
" class should be less or equal to the original number of"
" samples. Originally, there is 25 samples and 70 samples"
" are asked.")
with raises(ValueError, match=error_regex):
check_ratio(ratio, y, 'under-sampling')
def test_ratio_dict_error():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = {1: -100, 2: 50, 3: 25}
with raises(ValueError, match="in a class cannot be negative."):
check_ratio(ratio, y, 'under-sampling')
ratio = {10: 10}
with raises(ValueError, match="are not present in the data."):
check_ratio(ratio, y, 'over-sampling')
ratio = {1: 45, 2: 100, 3: 70}
error_regex = ("With over-sampling methods, the number of samples in a"
" class should be greater or equal to the original number"
" of samples. Originally, there is 50 samples and 45"
" samples are asked.")
with raises(ValueError, match=error_regex):
check_ratio(ratio, y, 'over-sampling')
error_regex = ("With under-sampling methods, the number of samples in a"
" class should be less or equal to the original number of"
" samples. Originally, there is 25 samples and 70 samples"
" are asked.")
with raises(ValueError, match=error_regex):
check_ratio(ratio, y, 'under-sampling')
def test_ratio_dict_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = {1: 30, 2: 45, 3: 25}
ratio_ = check_ratio(ratio, y, 'under-sampling')
assert ratio_ == ratio
def test_ratio_majority_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('majority', y, 'under-sampling')
assert ratio == {2: 25}
def test_ratio_minority_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('minority', y, 'over-sampling')
assert ratio == {3: 75}
def test_ratio_not_minority_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('not minority', y, 'under-sampling')
assert ratio == {1: 25, 2: 25}
ratio = check_ratio('auto', y, 'under-sampling')
assert ratio == {1: 25, 2: 25}
def test_ratio_not_minority_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('not minority', y, 'over-sampling')
assert ratio == {1: 50, 2: 0}
def test_ratio_all_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('all', y, 'over-sampling')
assert_equal(ratio, {1: 50, 2: 0, 3: 75})
ratio = check_ratio('auto', y, 'over-sampling')
assert_equal(ratio, {1: 50, 2: 0, 3: 75})
def test_ratio_majority_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('majority', y, 'under-sampling')
assert ratio == {2: 25}
def test_ratio_all_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('all', y, 'under-sampling')
assert ratio == {1: 25, 2: 25, 3: 25}
def test_ratio_not_minority_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('not minority', y, 'over-sampling')
assert ratio == {1: 50, 2: 0}
def test_ratio_not_minority_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('not minority', y, 'under-sampling')
assert ratio == {1: 25, 2: 25}
ratio = check_ratio('auto', y, 'under-sampling')
assert ratio == {1: 25, 2: 25}
def test_ratio_minority_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('minority', y, 'over-sampling')
assert ratio == {3: 75}
def test_ratio_dict_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = {1: 30, 2: 45, 3: 25}
ratio_ = check_ratio(ratio, y, 'under-sampling')
assert ratio_ == ratio
def test_ratio_float_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = 0.5
ratio_ = check_ratio(ratio, y, 'over-sampling')
assert_equal(ratio_, {1: 0, 3: 25})
def test_ratio_all_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
for each in ('all', 'auto'):
assert check_ratio(each, y, 'over-sampling') == {1: 50, 2: 0, 3: 75}
def test_check_ratio(ratio, sampling_type, expected_ratio, target):
with pytest.warns(DeprecationWarning, match="check_ratio is deprecated"):
ratio_ = check_ratio(ratio, target, sampling_type)
assert ratio_ == expected_ratio
def test_ratio_all_over_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
for each in ('all', 'auto'):
assert check_ratio(each, y, 'over-sampling') == {1: 50, 2: 0, 3: 75}
def test_ratio_majority_over_sampling():
error_regex = "'ratio'='majority' cannot be used with over-sampler."
with raises(ValueError, match=error_regex):
check_ratio('majority', np.array([1, 2, 3]), 'over-sampling')
def test_ratio_majority_over_sampling():
error_regex = "'ratio'='majority' cannot be used with over-sampler."
with raises(ValueError, match=error_regex):
check_ratio('majority', np.array([1, 2, 3]), 'over-sampling')
def test_check_ratio(ratio, sampling_type, expected_ratio, target):
with pytest.warns(DeprecationWarning, match="check_ratio is deprecated"):
ratio_ = check_ratio(ratio, target, sampling_type)
assert ratio_ == expected_ratio
def test_ratio_all_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = check_ratio('all', y, 'under-sampling')
assert ratio == {1: 25, 2: 25, 3: 25}
def test_ratio_float_under_sampling():
y = np.array([1] * 50 + [2] * 100 + [3] * 25)
ratio = 0.5
ratio_ = check_ratio(ratio, y, 'under-sampling')
assert_equal(ratio_, {1: 50, 2: 50})
