本文整理汇总了Python中imblearn.under_sampling.InstanceHardnessThreshold.fit_resample方法的典型用法代码示例。如果您正苦于以下问题：Python InstanceHardnessThreshold.fit_resample方法的具体用法？Python InstanceHardnessThreshold.fit_resample怎么用？Python InstanceHardnessThreshold.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.InstanceHardnessThreshold的用法示例。
在下文中一共展示了InstanceHardnessThreshold.fit_resample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_iht_fit_resample
# 需要导入模块: from imblearn.under_sampling import InstanceHardnessThreshold [as 别名]
# 或者: from imblearn.under_sampling.InstanceHardnessThreshold import fit_resample [as 别名]
def test_iht_fit_resample():
iht = InstanceHardnessThreshold(ESTIMATOR, random_state=RND_SEED)
X_resampled, y_resampled = iht.fit_resample(X, Y)
X_gt = np.array([[-0.3879569, 0.6894251], [0.91542919, -0.65453327], [
-0.65571327, 0.42412021
], [1.06446472, -1.09279772], [0.30543283, -0.02589502], [
-0.00717161, 0.00318087
], [-0.09322739, 1.28177189], [-0.77740357, 0.74097941],
[-0.43877303, 1.07366684], [-0.85795321, 0.82980738],
[-0.18430329, 0.52328473], [-0.28305528, 0.30284991]])
y_gt = np.array([0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1])
assert_array_equal(X_resampled, X_gt)
assert_array_equal(y_resampled, y_gt)示例2: test_iht_fit_resample_wrong_class_obj
# 需要导入模块: from imblearn.under_sampling import InstanceHardnessThreshold [as 别名]
# 或者: from imblearn.under_sampling.InstanceHardnessThreshold import fit_resample [as 别名]
def test_iht_fit_resample_wrong_class_obj():
from sklearn.cluster import KMeans
est = KMeans()
iht = InstanceHardnessThreshold(estimator=est, random_state=RND_SEED)
with raises(ValueError, match="Invalid parameter `estimator`"):
iht.fit_resample(X, Y)示例3: zip
# 需要导入模块: from imblearn.under_sampling import InstanceHardnessThreshold [as 别名]
# 或者: from imblearn.under_sampling.InstanceHardnessThreshold import fit_resample [as 别名]
# Two subplots, unpack the axes array immediately
f, axs = plt.subplots(2, 2)
axs = [a for ax in axs for a in ax]
for ax, sampling_strategy in zip(axs, (0,
{1: 25, 0: 10},
{1: 14, 0: 10},
{1: 10, 0: 10})):
if sampling_strategy == 0:
c0, c1 = plot_resampling(ax, X_vis, y, 'Original set')
else:
iht = InstanceHardnessThreshold(sampling_strategy=sampling_strategy,
estimator=LogisticRegression(),
return_indices=True)
X_res, y_res, idx_res = iht.fit_resample(X, y)
X_res_vis = pca.transform(X_res)
plot_resampling(ax, X_res_vis, y_res,
'Instance Hardness Threshold ({})'
.format(sampling_strategy))
# plot samples which have been removed
idx_samples_removed = np.setdiff1d(np.arange(X_vis.shape[0]),
idx_res)
c3 = ax.scatter(X_vis[idx_samples_removed, 0],
X_vis[idx_samples_removed, 1],
alpha=.2, label='Removed samples')
plt.figlegend((c0, c1, c3), ('Class #0', 'Class #1', 'Removed samples'),
loc='lower center', ncol=3, labelspacing=0.)
plt.tight_layout(pad=3)
plt.show()