本文整理汇总了Python中imblearn.over_sampling.SMOTE.fit方法的典型用法代码示例。如果您正苦于以下问题：Python SMOTE.fit方法的具体用法？Python SMOTE.fit怎么用？Python SMOTE.fit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.over_sampling.SMOTE的用法示例。
在下文中一共展示了SMOTE.fit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_sample_svm
# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE import fit [as 别名]
def test_sample_svm():
"""Test sample function with SVM SMOTE."""
# Create the object
kind = 'svm'
smote = SMOTE(random_state=RND_SEED, kind=kind)
# Fit the data
smote.fit(X, Y)
X_resampled, y_resampled = smote.fit_sample(X, Y)
X_gt = np.array([[0.11622591, -0.0317206], [0.77481731, 0.60935141],
[1.25192108, -0.22367336], [0.53366841, -0.30312976],
[1.52091956, -0.49283504], [-0.28162401, -2.10400981],
[0.83680821, 1.72827342], [0.3084254, 0.33299982],
[0.70472253, -0.73309052], [0.28893132, -0.38761769],
[1.15514042, 0.0129463], [0.88407872, 0.35454207],
[1.31301027, -0.92648734], [-1.11515198, -0.93689695],
[-0.18410027, -0.45194484], [0.9281014, 0.53085498],
[-0.14374509, 0.27370049], [-0.41635887, -0.38299653],
[0.08711622, 0.93259929], [1.70580611, -0.11219234],
[0.47436888, -0.2645749], [1.07844561, -0.19435291],
[1.44015515, -1.30621303]])
y_gt = np.array(
[0, 1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 0, 0])
assert_array_almost_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)示例2: test_sample_wrong_X
# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE 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 = SMOTE(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3: test_smote_fit
# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE import fit [as 别名]
def test_smote_fit():
"""Test the fitting method"""
# Create the object
smote = SMOTE(random_state=RND_SEED)
# Fit the data
smote.fit(X, Y)
# Check if the data information have been computed
assert_equal(smote.min_c_, 0)
assert_equal(smote.maj_c_, 1)
assert_equal(smote.stats_c_[0], 8)
assert_equal(smote.stats_c_[1], 12)示例4: test_sample_regular
# 需要导入模块: from imblearn.over_sampling import SMOTE [as 别名]
# 或者: from imblearn.over_sampling.SMOTE import fit [as 别名]
def test_sample_regular():
"""Test sample function with regular SMOTE."""
# Create the object
kind = 'regular'
smote = SMOTE(random_state=RND_SEED, kind=kind)
# 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_reg_x.npy'))
y_gt = np.load(os.path.join(currdir, 'data', 'smote_reg_y.npy'))
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)