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。到目前为止我的工作import numpy as npimport matplotlib.pyplot as pltfrom sklearn.linear_model import LogisticRegressionfrom sklearn import datasetsfrom matplotlib.colors import ListedColormapcmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])# import some data to play withiris = datasets.load_iris()X = iris.data[:, :2]  # we only take the first two features.Y = iris.targetlogreg = 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() + .5y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5h = .02  # step size in the meshxx, 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 plotZ = Z.reshape(xx.shape)plt.figure(1, figsize=(4, 3))plt.pcolormesh(xx, yy, Z, cmap=cmap_light)# Plot also the training pointsplt.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()但是我发现它非常难以阅读。我想在左上角为每个分类和图例添加其他标记。就像下图所示：你知道我该如何改变吗？我玩过marker ='s', marker='x'，但这些改变了散点图上的所有点，而不是一个特定的分类。

Answer 1

由于您正在使用分类值进行绘图，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：

Answer 2

• X我发现从&创建数据框Y，然后用 绘制数据点更容易seaborn.scatterplot。

  • seaborn是 matplotlib 的高级 api

  • 如如何从 k-近邻预测中提取边界值中所示，数据框列可用于指定要拟合的所有数据以及 x 和 y 的最小值和最大值。

加载并设置数据

import numpy as np

import matplotlib.pyplot as plt  # version 3.3.1

from sklearn.linear_model import LogisticRegression

from sklearn import datasets

from matplotlib.colors import ListedColormap

import seaborn  # versuin 0.11.0

import pandas  # version 1.1.3

cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])

cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])

# seaborn.scatterplot palette parameter takes a list

palette = ['#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

# add X & Y to dataframe

df = pd.DataFrame(X, columns=iris.feature_names[:2])

df['label'] = Y

# map the number values to the species name and add it to the dataframe

species_map = dict(zip(range(3), iris.target_names))

df['species'] = df.label.map(species_map)

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)

绘制数据图

plt.figure(1, figsize=(8, 6))

plt.pcolormesh(xx, yy, Z, cmap=cmap_light, shading='auto')

# Plot also the training points

# add data points using seaborn

sns.scatterplot(data=df, x='sepal length (cm)', y='sepal width (cm)', hue='species',

                style='species', edgecolor='k', alpha=0.5, palette=palette, s=70)

# change legend location

plt.legend(title='Species', loc=2)

plt.xlim(xx.min(), xx.max())

plt.ylim(yy.min(), yy.max())

# plt.xticks(())

# plt.yticks(())

plt.show()

alpha=0.5与 , 一起使用sns.scatterplot，以显示 和 的某些值'versicolor'重叠'virginica'。

如果species图例需要标签而不是名称，请更改hue='species'为hue='label'。

Answer 3

您需要将单个调用更改plt.scatter为每个标记类型的一个调用，因为 matplotlib 不允许像颜色那样传递多个标记类型。

情节代码变成了类似的东西

# 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

X0 = X[Y==0]

X1 = X[Y==1]

X2 = X[Y==2]

Y0 = Y[Y==0]

Y1 = Y[Y==1]

Y2 = Y[Y==2]

plt.scatter(X0[:, 0], X0[:,1], marker='s',color="red")

plt.scatter(X1[:, 0], X1[:,1], marker='x',color="blue")

plt.scatter(X2[:, 0], X2[:,1], marker='o',color="green")

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()

您可以在其中单独设置每个类别的标记类型和颜色。您还可以为标记类型创建一个列表，为颜色创建另一个列表，并使用循环。