python - 如何在散点图上有多个分类标记

Question

我想训练逻辑回归模型，然后创建一个以特定方式显示边界线的图。

我目前的工作

import numpy as np
import matplotlib.pyplot as plt
from sklearn.linear_model import LogisticRegression
from sklearn import datasets
from matplotlib.colors import ListedColormap

cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])
cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

# import some data to play with
iris = datasets.load_iris()
X = iris.data[:, :2]  # we only take the first two features.
Y = iris.target

logreg = LogisticRegression(C=1e5)

# Create an instance of Logistic Regression Classifier and fit the data.
logreg.fit(X, Y)

# Plot the decision boundary. For that, we will assign a color to each
# point in the mesh [x_min, x_max]x[y_min, y_max].
x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5
h = .02  # step size in the mesh
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
Z = logreg.predict(np.c_[xx.ravel(), yy.ravel()])

# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(4, 3))

plt.pcolormesh(xx, yy, Z, cmap=cmap_light)
# Plot also the training points

plt.scatter(X[:, 0], X[:,1], c=Y, marker='x',edgecolors='k', cmap=cmap_bold)
plt.xlabel('Sepal length'),
plt.ylabel('Sepal width')

plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
plt.xticks(())
plt.yticks(())

plt.show()

但是我觉得它很难读。我想在左上角为每个分类和图例添加其他标记。就像下图一样:

你知道我该如何改变它吗？我玩过 marker ='s'、marker='x'，但它们改变了散点图上的所有点，而不是一个特定的分类。

Answer 1

由于您使用的是分类值，因此您可以单独绘制每个类:

# Replace this
# plt.scatter(X[:, 0], X[:,1], c=Y, marker='x',edgecolors='k', cmap=cmap_bold)
# with this

markers = 'sxo'
for m,i in zip(markers,np.unique(Y)):
    mask = Y==i
    plt.scatter(X[mask, 0], X[mask,1], c=cmap_bold.colors[i],
                marker=m,edgecolors='k', label=i)
plt.legend()

输出: