本文整理汇总了Python中imblearn.under_sampling.NearMiss.fit_resample方法的典型用法代码示例。如果您正苦于以下问题：Python NearMiss.fit_resample方法的具体用法？Python NearMiss.fit_resample怎么用？Python NearMiss.fit_resample使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类imblearn.under_sampling.NearMiss的用法示例。
在下文中一共展示了NearMiss.fit_resample方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_nm_fit_resample_auto
# 需要导入模块: from imblearn.under_sampling import NearMiss [as 别名]
# 或者: from imblearn.under_sampling.NearMiss import fit_resample [as 别名]
def test_nm_fit_resample_auto():
sampling_strategy = 'auto'
X_gt = [
np.array([[0.91464286, 1.61369212], [-0.80809175, -1.09917302], [
-0.20497017, -0.26630228
], [-0.05903827, 0.10947647], [0.03142011, 0.12323596],
[-0.60413357, 0.24628718], [0.50701028, -0.17636928],
[0.4960075, 0.86130762], [0.45713638, 1.31069295]]),
np.array([[0.91464286, 1.61369212], [-0.80809175, -1.09917302], [
-0.20497017, -0.26630228
], [-0.05903827, 0.10947647], [0.03142011, 0.12323596],
[-0.60413357, 0.24628718], [0.50701028, -0.17636928],
[0.4960075, 0.86130762], [0.45713638, 1.31069295]]),
np.array([[0.91464286, 1.61369212], [-0.80809175, -1.09917302], [
-0.20497017, -0.26630228
], [1.17737838, -0.2002118], [-0.60413357, 0.24628718],
[0.03142011, 0.12323596], [1.15157493, -1.2981518],
[-0.54619583, 1.73009918], [0.99272351, -0.11631728]])
]
y_gt = [
np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]),
np.array([0, 0, 0, 1, 1, 1, 2, 2, 2]),
np.array([0, 0, 0, 1, 1, 1, 2, 2, 2])
]
for version_idx, version in enumerate(VERSION_NEARMISS):
nm = NearMiss(sampling_strategy=sampling_strategy, version=version)
X_resampled, y_resampled = nm.fit_resample(X, Y)
assert_array_equal(X_resampled, X_gt[version_idx])
assert_array_equal(y_resampled, y_gt[version_idx])示例2: test_nm_wrong_nn_obj
# 需要导入模块: from imblearn.under_sampling import NearMiss [as 别名]
# 或者: from imblearn.under_sampling.NearMiss import fit_resample [as 别名]
def test_nm_wrong_nn_obj():
sampling_strategy = 'auto'
nn = 'rnd'
nm = NearMiss(
sampling_strategy=sampling_strategy,
version=VERSION_NEARMISS,
return_indices=True,
n_neighbors=nn)
with raises(ValueError, match="has to be one of"):
nm.fit_resample(X, Y)
nn3 = 'rnd'
nn = NearestNeighbors(n_neighbors=3)
nm3 = NearMiss(
sampling_strategy=sampling_strategy,
version=3,
return_indices=True,
n_neighbors=nn,
n_neighbors_ver3=nn3)
with raises(ValueError, match="has to be one of"):
nm3.fit_resample(X, Y)示例3: test_nearmiss_wrong_version
# 需要导入模块: from imblearn.under_sampling import NearMiss [as 别名]
# 或者: from imblearn.under_sampling.NearMiss import fit_resample [as 别名]
def test_nearmiss_wrong_version():
version = 1000
nm = NearMiss(version=version)
with raises(ValueError, match="must be 1, 2 or 3"):
nm.fit_resample(X, Y)示例4: test_deprecation_random_state
# 需要导入模块: from imblearn.under_sampling import NearMiss [as 别名]
# 或者: from imblearn.under_sampling.NearMiss import fit_resample [as 别名]
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)