本文整理汇总了Python中imblearn.combine.SMOTETomek.fit方法的典型用法代码示例。如果您正苦于以下问题：Python SMOTETomek.fit方法的具体用法？Python SMOTETomek.fit怎么用？Python SMOTETomek.fit使用的例子？那么, 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.combine.SMOTETomek的用法示例。
在下文中一共展示了SMOTETomek.fit方法的3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_sample_wrong_X
# 需要导入模块: from imblearn.combine import SMOTETomek [as 别名]
# 或者: from imblearn.combine.SMOTETomek import fit [as 别名]
def test_sample_wrong_X():
"""Test either if an error is raised when X is different at fitting
and sampling"""
# Create the object
sm = SMOTETomek(random_state=RND_SEED)
sm.fit(X, Y)
assert_raises(RuntimeError, sm.sample, np.random.random((100, 40)), np.array([0] * 50 + [1] * 50))示例2: test_sample_regular
# 需要导入模块: from imblearn.combine import SMOTETomek [as 别名]
# 或者: from imblearn.combine.SMOTETomek import fit [as 别名]
def test_sample_regular():
"""Test sample function with regular SMOTE."""
# Create the object
smote = SMOTETomek(random_state=RND_SEED)
# Fit the data
smote.fit(X, Y)
X_resampled, y_resampled = smote.fit_sample(X, Y)
currdir = os.path.dirname(os.path.abspath(__file__))
X_gt = np.load(os.path.join(currdir, 'data', 'smote_tomek_reg_x.npy'))
y_gt = np.load(os.path.join(currdir, 'data', 'smote_tomek_reg_y.npy'))
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)示例3: test_sample_regular_half
# 需要导入模块: from imblearn.combine import SMOTETomek [as 别名]
# 或者: from imblearn.combine.SMOTETomek import fit [as 别名]
def test_sample_regular_half():
"""Test sample function with regular SMOTE and a ratio of 0.5."""
# Create the object
ratio = 0.5
smote = SMOTETomek(ratio=ratio, random_state=RND_SEED)
# Fit the data
smote.fit(X, Y)
X_resampled, y_resampled = smote.fit_sample(X, Y)
currdir = os.path.dirname(os.path.abspath(__file__))
X_gt = np.load(os.path.join(currdir, "data", "smote_tomek_reg_x_05.npy"))
y_gt = np.load(os.path.join(currdir, "data", "smote_tomek_reg_y_05.npy"))
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)