如何在散点图上具有多个分类标记

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

SELECT 332 IN UNNEST([663, 332, 334]) AS contains_value;

但是我发现它很难读。我想在左上角为每个分类和图例添加其他标记。就像下面的图片一样：

您知道我该如何更改吗？我玩过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() ，marker ='s'，但是它们改变了散点图上的所有点，而不是一种特定的分类。

Answer 1

由于要使用分类值进行绘制，因此可以分别绘制每个类：

# 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

您需要将每种标记类型的一次调用更改为plt.scatter，因为与颜色一样，matplotlib不允许传递多种标记类型。

情节代码变成类似

# 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()

，您可以分别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。

Answer 3

• 我发现用X和Y创建数据框，然后用seaborn.scatterplot绘制数据点比较容易。
  • seaborn是matplotlib的高级api
  • 如How to extract the boundary values from k-nearest neighbors predict所示，dataframe列可用于指定所有适合拟合的数据，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

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sns.scatterplot一起使用，以显示'versicolor'和'virginica'的某些值重叠。
• 如果图例需要species标签而不是名称，请将hue='species'更改为hue='label'。