标签传播圈：学习复杂结构#

LabelPropagation学习复杂的内部结构以演示“多管学习”的示例。外圆应标记为“红色”，内圆应标记为“蓝色”。由于两个标签组都位于各自不同的形状内，因此我们可以看到标签围绕圆正确传播。

# Authors: The scikit-learn developers
# SPDX-License-Identifier: BSD-3-Clause

我们生成一个具有两个同心圆的数据集。此外，数据集的每个样本都关联一个标签，即：0（属于外圆）、1（属于内圆）和-1（未知）。在这里，除两个标签外的所有标签都被标记为未知。

import numpy as np

from sklearn.datasets import make_circles

n_samples = 200
X, y = make_circles(n_samples=n_samples, shuffle=False)
outer, inner = 0, 1
labels = np.full(n_samples, -1.0)
labels[0] = outer
labels[-1] = inner

绘制原始数据

import matplotlib.pyplot as plt

plt.figure(figsize=(4, 4))
plt.scatter(
    X[labels == outer, 0],
    X[labels == outer, 1],
    color="navy",
    marker="s",
    lw=0,
    label="outer labeled",
    s=10,
)
plt.scatter(
    X[labels == inner, 0],
    X[labels == inner, 1],
    color="c",
    marker="s",
    lw=0,
    label="inner labeled",
    s=10,
)
plt.scatter(
    X[labels == -1, 0],
    X[labels == -1, 1],
    color="darkorange",
    marker=".",
    label="unlabeled",
)
plt.legend(scatterpoints=1, shadow=False, loc="center")
_ = plt.title("Raw data (2 classes=outer and inner)")

的目的 LabelSpreading 是将标签与最初未知标签的样本关联起来。

from sklearn.semi_supervised import LabelSpreading

label_spread = LabelSpreading(kernel="knn", alpha=0.8)
label_spread.fit(X, labels)

LabelSpreading(alpha=0.8, kernel='knn')

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现在，当标签未知时，我们可以检查哪些标签与每个样本关联。

output_labels = label_spread.transduction_
output_label_array = np.asarray(output_labels)
outer_numbers = (output_label_array == outer).nonzero()[0]
inner_numbers = (output_label_array == inner).nonzero()[0]

plt.figure(figsize=(4, 4))
plt.scatter(
    X[outer_numbers, 0],
    X[outer_numbers, 1],
    color="navy",
    marker="s",
    lw=0,
    s=10,
    label="outer learned",
)
plt.scatter(
    X[inner_numbers, 0],
    X[inner_numbers, 1],
    color="c",
    marker="s",
    lw=0,
    s=10,
    label="inner learned",
)
plt.legend(scatterpoints=1, shadow=False, loc="center")
plt.title("Labels learned with Label Spreading (KNN)")
plt.show()

Labels learned with Label Spreading (KNN)

Total running time of the script: （0分0.131秒）