Note

Go to the end to download the full example code. or to run this example in your browser via Binder

在鸢尾花数据集上绘制多类SGD#

在鸢尾花数据集上绘制多类SGD的决策面。对应于三个一对多（OVA）分类器的超平面由虚线表示。

import matplotlib.pyplot as plt
import numpy as np

from sklearn import datasets
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.linear_model import SGDClassifier

# 导入一些数据来玩玩
iris = datasets.load_iris()

# 我们只取前两个特征。我们可以通过使用一个二维数据集来避免这种丑陋的切片操作。
X = iris.data[:, :2]
y = iris.target
colors = "bry"

# shuffle
idx = np.arange(X.shape[0])
np.random.seed(13)
np.random.shuffle(idx)
X = X[idx]
y = y[idx]

# 标准化
mean = X.mean(axis=0)
std = X.std(axis=0)
X = (X - mean) / std

clf = SGDClassifier(alpha=0.001, max_iter=100).fit(X, y)
ax = plt.gca()
DecisionBoundaryDisplay.from_estimator(
    clf,
    X,
    cmap=plt.cm.Paired,
    ax=ax,
    response_method="predict",
    xlabel=iris.feature_names[0],
    ylabel=iris.feature_names[1],
)
plt.axis("tight")

# 还要绘制训练点
for i, color in zip(clf.classes_, colors):
    idx = np.where(y == i)
    plt.scatter(
        X[idx, 0],
        X[idx, 1],
        c=color,
        label=iris.target_names[i],
        edgecolor="black",
        s=20,
    )
plt.title("Decision surface of multi-class SGD")
plt.axis("tight")

# 绘制三个一对多分类器
xmin, xmax = plt.xlim()
ymin, ymax = plt.ylim()
coef = clf.coef_
intercept = clf.intercept_


def plot_hyperplane(c, color):
    def line(x0):
        return (-(x0 * coef[c, 0]) - intercept[c]) / coef[c, 1]

    plt.plot([xmin, xmax], [line(xmin), line(xmax)], ls="--", color=color)


for i, color in zip(clf.classes_, colors):
    plot_hyperplane(i, color)
plt.legend()
plt.show()