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绘制多项式和一对其余逻辑回归#

绘制多项式和一对其余逻辑回归的决策面。与三个一对其余（OVR）分类器对应的超平面用虚线表示。

training score : 0.995 (multinomial)
training score : 0.976 (ovr)

# 作者：scikit-learn 开发者
# SPDX-License-Identifier: BSD-3-Clause

import matplotlib.pyplot as plt
import numpy as np

from sklearn.datasets import make_blobs
from sklearn.inspection import DecisionBoundaryDisplay
from sklearn.linear_model import LogisticRegression
from sklearn.multiclass import OneVsRestClassifier

# 制作用于分类的3类数据集
centers = [[-5, 0], [0, 1.5], [5, -1]]
X, y = make_blobs(n_samples=1000, centers=centers, random_state=40)
transformation = [[0.4, 0.2], [-0.4, 1.2]]
X = np.dot(X, transformation)

for multi_class in ("multinomial", "ovr"):
    clf = LogisticRegression(solver="sag", max_iter=100, random_state=42)
    if multi_class == "ovr":
        clf = OneVsRestClassifier(clf)
    clf.fit(X, y)

    # 打印训练分数
    print("training score : %.3f (%s)" % (clf.score(X, y), multi_class))

    _, ax = plt.subplots()
    DecisionBoundaryDisplay.from_estimator(
        clf, X, response_method="predict", cmap=plt.cm.Paired, ax=ax
    )
    plt.title("Decision surface of LogisticRegression (%s)" % multi_class)
    plt.axis("tight")

    # 还要绘制训练点
    colors = "bry"
    for i, color in zip(clf.classes_, colors):
        idx = np.where(y == i)
        plt.scatter(X[idx, 0], X[idx, 1], c=color, edgecolor="black", s=20)

    # 绘制三个一对多分类器
    xmin, xmax = plt.xlim()
    ymin, ymax = plt.ylim()
    if multi_class == "ovr":
        coef = np.concatenate([est.coef_ for est in clf.estimators_])
        intercept = np.concatenate([est.intercept_ for est in clf.estimators_])
    else:
        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.show()